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GTC On-Demand

Deep Learning and AI
Presentation
Media
Opening Keynote
Jensen Huang (NVIDIA)
The 2018 GTC opening keynote is delivered by the NVIDIA Founder and CEO, Jensen Huang, speaking on the future of computing. ...Read More

The 2018 GTC opening keynote is delivered by the NVIDIA Founder and CEO, Jensen Huang, speaking on the future of computing.

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Keywords:
Deep Learning and AI, GTC Silicon Valley 2018 - ID S8885
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GTC Taiwan Opening Keynote 2018
Jensen Huang (NVIDIA)
The 2018 GTC Taiwan opening keynote is delivered by NVIDIA Founder and CEO, Jensen Huang.
The 2018 GTC Taiwan opening keynote is delivered by NVIDIA Founder and CEO, Jensen Huang.  Back
 
Keywords:
Deep Learning and AI, Autonomous Vehicles, AI and DL Research, GTC Taiwan 2018 - ID STW8000
Streaming:
 
Keynote
Jensen Huang (NVIDIA)
Don't miss this keynote from NVIDIA Founder & CEO, Jensen Huang, as he speaks on the future of computing. ...Read More

Don't miss this keynote from NVIDIA Founder & CEO, Jensen Huang, as he speaks on the future of computing.

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Keywords:
Deep Learning and AI, Data Center and Cloud Infrastructure, Virtual Reality and Augmented Reality, Self-Driving Cars, Intelligent Video Analytics and Smart Cities, GTC Silicon Valley 2017 - ID S7820
Streaming:
 
Keynote
Jensen Huang (NVIDIA)
Don't miss this keynote from NVIDIA Founder & CEO, Jensen Huang, as he speaks on the future of computing.  ...Read More

Don't miss this keynote from NVIDIA Founder & CEO, Jensen Huang, as he speaks on the future of computing. 

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Keywords:
Deep Learning and AI, Virtual Reality and Augmented Reality, Data Center and Cloud Infrastructure, Autonomous Vehicles, Intelligent Video Analytics and Smart Cities, GTC Israel 2017 - ID SIL7001
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Keynote
Jensen Huang (NVIDIA)
 
Keywords:
Deep Learning and AI, GTC Taiwan 2017 - ID GTCT7101
Streaming:
Leadership in AI
Presentation
Media
AI: Transforming Your Work and the World Now
Greg Estes (NVIDIA)
Artificial intelligence is changing the world at an accelerating pace. AI has quickly jumped from research labs to business and consumer applications. In this keynote, Greg will share the latest developments in AI for transportation, robotics, manufa ...Read More
Artificial intelligence is changing the world at an accelerating pace. AI has quickly jumped from research labs to business and consumer applications. In this keynote, Greg will share the latest developments in AI for transportation, robotics, manufacturing, healthcare and government.  Back
 
Keywords:
Leadership in AI, GTC Washington D.C. 2017 - ID DC7114
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AI in Healthcare
Presentation
Media
Harnessing AI in Healthcare
Keith Dreyer (Massachusetts General Hospital/Harvard Medical School)
As computers outperform humans at complex cognitive tasks, disruptive innovation will increasingly remap the familiar with waves of creative destruction. And in healthcare, nowhere is this more apparent or imminent than at the crossroads of Radiology ...Read More
As computers outperform humans at complex cognitive tasks, disruptive innovation will increasingly remap the familiar with waves of creative destruction. And in healthcare, nowhere is this more apparent or imminent than at the crossroads of Radiology and the emerging field of Clinical Data Science. As leaders in our field, we must shepherd the innovations of cognitive computing by defining its role within diagnostic imaging, while first and foremost ensuring the continued safety of our patients. If we are dismissive, defensive or self-motivated - industry, payers and provider entities will innovate around us achieving different forms of disruption, optimized to serve their own needs. To maintain our leadership position, as we enter the era of machine learning, it is essential that we serve our patients by directly managing the use of clinical data science towards the improvement of carea position which will only strengthen our relevance in the care process.  Back
 
Keywords:
AI in Healthcare, GTC Washington D.C. 2017 - ID DC7240
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Deep Learning and AI
Presentation
Media
Keynote
Jensen Huang (NVIDIA)
Don't miss this keynote from NVIDIA Founder & CEO, Jensen Huang, as he speaks on the future of computing.  ...Read More

Don't miss this keynote from NVIDIA Founder & CEO, Jensen Huang, as he speaks on the future of computing. 

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Keywords:
Deep Learning and AI, Accelerated Analytics, AI Startup, GTC Japan 2017 - ID 1000
Streaming:
 
Keynote
Jensen Huang (NVIDIA)
Don't miss this keynote from NVIDIA Founder & CEO, Jensen Huang, as he speaks on the future of computing.  ...Read More

Don't miss this keynote from NVIDIA Founder & CEO, Jensen Huang, as he speaks on the future of computing. 

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Keywords:
Deep Learning and AI, Autonomous Vehicles, Self-Driving Cars, Data Center and Cloud Infrastructure, GTC Europe 2017 - ID 23000
Streaming:
 
A New Computing Era
Jensen Huang (NVIDIA)
N/A ...Read More

N/A

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Keywords:
Deep Learning and AI, GTC China 2017 - ID 100
Streaming:
Intelligent Machines and IoT
Presentation
Media
Keynote Address
Bill Dally (Chief Scientist, NVIDIA)
Opening Keynote Speech ...Read More

Opening Keynote Speech

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Keywords:
Intelligent Machines and IoT, GTC Washington D.C. 2016 - ID DCS16158
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Federal
Presentation
Media
Advancing the Frontiers of Science
France Cordova (Director, National Science Foundation)
The National Science Foundation (NSF) is an independent federal agency that supports fundamental research and education across all fields of science and engineering. With an annual budget of $7.5 billion, NSF awards grants to nearly 2,000 colleg ...Read More

The National Science Foundation (NSF) is an independent federal agency that supports fundamental research and education across all fields of science and engineering. With an annual budget of $7.5 billion, NSF awards grants to nearly 2,000 colleges, universities and other institutions in all 50 states. Hear how NSF is advancing discovery and technological innovation in all fields, including artificial intelligence, to keep the United States at the forefront of global science and engineering leadership.

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Keywords:
Federal, GTC Washington D.C. 2016 - ID DCS16164
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Healthcare and Life Sciences
Presentation
Media
Cancer Research and Technology Cancer Moonshot Project
Jerry Lee (Deputy Director for Cancer Research and Technology , Office of the Vice President)
In this keynote, we'll show how the Cancer Moonshot Task Force under Vice President Biden is unleashing the power of data to help end cancer as we know it. We'll discuss global efforts inspired by the Cancer Moonshot that will empower A. ...Read More

In this keynote, we'll show how the Cancer Moonshot Task Force under Vice President Biden is unleashing the power of data to help end cancer as we know it. We'll discuss global efforts inspired by the Cancer Moonshot that will empower A.I. and deep learning for oncology with larger and more accessible datasets.

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Keywords:
Healthcare and Life Sciences, GTC Washington D.C. 2016 - ID DCS16165
Streaming:
Intelligent Machines and IoT
Presentation
Media
The Economic Implications of Artificial Intelligence
Jason Furman (Chairman, White House Council of Economic Advisors , Office of the President of the United States)
 
Keywords:
Intelligent Machines and IoT, GTC Washington D.C. 2016 - ID DCS16182
Streaming:
Deep Learning and AI
Presentation
Media
Opening Keynote.
David Kirk (NVIDIA Fellow, NVIDIA)
Opening Keynote. ...Read More

Opening Keynote.

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Keywords:
Deep Learning and AI, AI Conference Australia 2016 - ID AUS6123
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Customer Keynote
Anton van den Hengel (Director, Australian Centre for Visual Technologies)
What New Results in Visual Question Answering Have to Say about Old AI ...Read More

What New Results in Visual Question Answering Have to Say about Old AI

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Keywords:
Deep Learning and AI, AI Conference Australia 2016 - ID AUS6124
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Welcome Speech
Amanda Caples (Lead Scientist and Deputy Secretary, Sector Development Division, Victorian Dept. Economic Development, Jobs, Transport and Resources)
Opening Keynote Speech ...Read More

Opening Keynote Speech

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Keywords:
Deep Learning and AI, AI Conference Australia 2016 - ID AUS6126
Streaming:
Virtual Reality and Augmented Reality
Presentation
Media
NVIDIA Keynote, CEO
Jen-Hsun Huang (NVIDIA)
 
Keywords:
Virtual Reality and Augmented Reality, Deep Learning and AI, Algorithms and Numerical Techniques, AI Startup, GTC Japan 2016 - ID 1000
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Machine Learning & Deep Learning
Presentation
Media
Opening Keynote
Jensen Huang (NVIDIA)
Don't miss GTC's opening keynote address from NVIDIA CEO and co-founder Jensen Huang. He'll discuss the latest breakthroughs in visual computing, including how NVIDIA is fueling the revolution in deep learning. ...Read More

Don't miss GTC's opening keynote address from NVIDIA CEO and co-founder Jensen Huang. He'll discuss the latest breakthroughs in visual computing, including how NVIDIA is fueling the revolution in deep learning.

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Keywords:
Machine Learning & Deep Learning, GTC Silicon Valley 2015 - ID S2000
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Large-Scale Deep Learning For Building Intelligent Computer Systems
Jeff Dean (Google)
Over the past few years, we have built large-scale computer systems for training neural networks, and then applied these systems to a wide variety of problems that have traditionally been very difficult for computers. We have made significant im ...Read More

Over the past few years, we have built large-scale computer systems for training neural networks, and then applied these systems to a wide variety of problems that have traditionally been very difficult for computers. We have made significant improvements in the state-of-the-art in many of these areas, and our software systems and algorithms have been used by dozens of different groups at Google to train state-of-the-art models for speech recognition, image recognition, various visual detection tasks, language modeling, language translation, and many other tasks. In this talk, I''ll highlight some of the distributed systems and algorithms that we use in order to train large models quickly. I''ll then discuss ways in which we have applied this work to a variety of problems in Google''s products, usually in close collaboration with other teams. This talk describes joint work with many people at Google.

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Keywords:
Machine Learning & Deep Learning, GTC Silicon Valley 2015 - ID S5817
 
Deep Learning: What's Next
Andrew Ng (Baidu)
Deep Learning has transformed many important tasks, including speech and image recognition. Deep Learning systems scale well by absorbing huge amounts of data to create accurate models. The computational resources afforded by GPUs have been instrumen ...Read More
Deep Learning has transformed many important tasks, including speech and image recognition. Deep Learning systems scale well by absorbing huge amounts of data to create accurate models. The computational resources afforded by GPUs have been instrumental to this scaling. However, as Deep Learning has become more mainstream, it has generated some hype, and has been linked to everything from world peace to evil killer robots. In this talk, Dr. Ng will help separate hype from reality, and discuss potential ways that Deep Learning technologies can benefit society in the short and long term.  Back
 
Keywords:
Machine Learning & Deep Learning, Computer Vision and Machine Vision, GTC Silicon Valley 2015 - ID S5818
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Keynote
Presentation
Media
Opening Keynote
Jensen Huang (NVIDIA)
Don''t miss the opening keynote feature Jensen Huang, Co-Founder, President, and CEO of NVIDIA. Hear about what''s next in visual computing, and preview disruptive technologies and exciting demonstrations across industries. ...Read More

Don''t miss the opening keynote feature Jensen Huang, Co-Founder, President, and CEO of NVIDIA. Hear about what''s next in visual computing, and preview disruptive technologies and exciting demonstrations across industries.

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Keywords:
Keynote, General Interest, GTC Silicon Valley 2014 - ID S4000
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Keynote: Video Games and the Future of Cognitive Enhancement
Adam Gazzaley (UCSF)
A fundamental challenge of modern society is the development of effective approaches to enhance brain function and cognition in both healthy and impaired individuals. For the healthy, this serves as a core mission of our educational system and f ...Read More

A fundamental challenge of modern society is the development of effective approaches to enhance brain function and cognition in both healthy and impaired individuals. For the healthy, this serves as a core mission of our educational system and for the cognitively impaired this is a critical goal of our medical system. Unfortunately, there are serious and growing concerns about the ability of either system to meet this challenge. I will describe an approach developed in our lab that uses custom-designed video games to achieve meaningful and sustainable cognitive enhancement (e.g., Anguera, et al. Nature 2013), as well the next stage of our research program, which uses video games integrated with technological innovations in software (e.g., brain computer interface algorithms, GPU computing) and hardware (e.g., virtual reality headsets, mobile EEG, transcranial electrical brain stimulation) to create a novel personalized closed loop system. I will share with you a vision of the future in which high-tech is used as an engine to enhance our brain''s information processing systems, thus reducing our reliance on non-specific drugs to treat neurological and psychiatric conditions and allowing us to better target our educational efforts. This keynote will be preceded by naming the winner of the CUDA Center of Excellence Achievement Award, winner for Best Poster, and the new CUDA Fellows, followed by the launch announcement of the Global Impact Award. (Award ceremony duration approximately 15 minutes).

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Keywords:
Keynote, Medical Imaging and Radiology, Video and Image Processing, GTC Silicon Valley 2014 - ID S4780
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Keynote: Using NVIDIA GPUs for Feature Film Production at Pixar
Danny Nahmias (Pixar), Dirk Van Gelder (Pixar)
This presentation will show how Pixar uses GPU technology to empower artists in the animation and lighting departments. By providing our artists with high-quality, interactive visual feedback, we enable them to spend more time making creative de ...Read More

This presentation will show how Pixar uses GPU technology to empower artists in the animation and lighting departments. By providing our artists with high-quality, interactive visual feedback, we enable them to spend more time making creative decisions. Animators interactively pose characters in order to create a performance. When features like displacement, fur, and shadows become critical for communicating the story, it is vital to be able to represent these visual elements in motion at interactive frame rates. We will show Presto, Pixar''s proprietary animation system, which uses GPU acceleration to deliver real-time feedback during the character animation process, using examples from Pixar''s recent films. Lighting artists place and adjust virtual lights to create the mood and tone of the scene as well as guide the audience''s attention. A physically-based illumination model allows these artists to create visually-rich imagery using simpler and more direct controls. We will demonstrate our interactive lighting preview tool, based on this model, built on NVIDIA''s OptiX framework, and fully integrated into our new Katana-based production workflow.

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Keywords:
Keynote, Media and Entertainment, GTC Silicon Valley 2014 - ID S4884
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General Interest
Presentation
Media
GTC Keynote with Jensen Huang, NVIDIA
Jensen Huang (NVIDIA)
Don''t miss the opening keynote feature Jensen Huang, Co-Founder, President, and CEO of NVIDIA. Hear about what''s next in computing and graphics, and preview disruptive technologies and exciting demonstrations across industries. ...Read More

Don''t miss the opening keynote feature Jensen Huang, Co-Founder, President, and CEO of NVIDIA. Hear about what''s next in computing and graphics, and preview disruptive technologies and exciting demonstrations across industries.

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Keywords:
General Interest, GTC Silicon Valley 2013 - ID S3900
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GTC Keynote featuring Erez Lieberman Aiden of Baylor College of Medicine and Rice University - Parallel Processing of the Genomes, by the Genomes and for the Genomes
Erez Lieberman Aiden (Department of Genetics at Baylor College of Medicine; Department of Computer Science of Computational and Applied Mathematics at Rice University)
The human genome is a sequence of 3 billion chemical letters inscribed in a molecule called DNA. Famously, short stretches (~10 letters, or a-base pairs) of DNA fold into a double helix. But what about longer pieces? How does a 2 meter long macr ...Read More

The human genome is a sequence of 3 billion chemical letters inscribed in a molecule called DNA. Famously, short stretches (~10 letters, or a-base pairs) of DNA fold into a double helix. But what about longer pieces? How does a 2 meter long macromolecule, the genome, fold up inside a 6 micrometer wide nucleus? And, once packed, how does the information contained in this ultra-dense structure remain accessible to the cell? This talk will discuss how the human genome folds in three dimensions, a folding enables the cell to access and process massive quantities of information in parallel. To probe how genomes fold, we developed Hi-C, together with collaborators at the Broad Institute and UMass Medical School. Hi-C couples proximity-dependent DNA ligation and massively parallel sequencing. To analyze our data and reconstruct the underlying folds, we, too must engage in massively parallel computation. I will describe how we use NVIDIA's CUDA technology to analyze our results and simulate the physical processes of genome folding and unfolding.

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Keywords:
General Interest, Developer - Algorithms, Bioinformatics & Genomics, GTC Silicon Valley 2013 - ID S3901
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GTC Keynote featuring Ralph Gilles of Chrysler
Ralph Gilles (Chrysler Group LLC)
Ralph Gilles, senior vice president of Product Design and president and CEO of SRT (Street and Racing Technology) Brand and Motorsports at Chrysler Group LLC and the mind behind some of the company most innovative products, will provide a behind ...Read More

Ralph Gilles, senior vice president of Product Design and president and CEO of SRT (Street and Racing Technology) Brand and Motorsports at Chrysler Group LLC and the mind behind some of the company most innovative products, will provide a behind-the-scenes look at the auto industry. Gilles will review how GPUs are used to advance every step of the automobile development process from the initial conceptual designs and engineering phases through product assembly and marketing. He will also discuss and how Chrysler Group utilizes GPUs and the latest technologies to build better, safer cars and reduce time to market.

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Keywords:
General Interest, Automotive, GTC Silicon Valley 2013 - ID S3902
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Opening Keynote
Jensen Huang (NVIDIA)
Do not miss the opening keynote, featuring Jensen Huang, CEO and Co-Founder of NVIDIA. Hear about what's next in computing and graphics, and preview disruptive technologies and exciting demonstrations from across industries. Jen-Hsun co-foun ...Read More

Do not miss the opening keynote, featuring Jensen Huang, CEO and Co-Founder of NVIDIA. Hear about what's next in computing and graphics, and preview disruptive technologies and exciting demonstrations from across industries. Jen-Hsun co-founded NVIDIA in 1993 and has served since its inception as president, chief executive officer and a member of the board of directors.

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Keywords:
General Interest, GTC Silicon Valley 2012 - ID S2000
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Day 2 Keynote: From Democratic Consensus to Cannibalistic Hordes: GPU Computing Reveals the Principles of Collective Behavior
Iain Couzin (Princeton University)
Collective behavior is one of the most pervasive features of the natural world. Our brains are composed of billions of interconnected cells communicating with chemical and electrical signals. We are integrated in our own human society. Elsewhere ...Read More

Collective behavior is one of the most pervasive features of the natural world. Our brains are composed of billions of interconnected cells communicating with chemical and electrical signals. We are integrated in our own human society. Elsewhere in the natural world a fish school convulses, as if one entity, when being attacked by a predator. How does individual behavior produce dynamic group-level properties? Do animal groups -or even cells in a tumor- function as some form of collective mind? How does socially contagious behavior spread through natural human crowds? In his keynote address, Prof. Iain D. Couzin, Professor of Ecology and Evolutionary Biology at Princeton University, will demonstrate how GPU computing has been pivotal in the study of collective behavior, helping reveal how collective action emerges in a wide range of groups from plague locusts to human crowds, and the critical role that uninformed, or weakly-opinionated, individuals play in democratic consensus decision-making.

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Keywords:
General Interest, GTC Silicon Valley 2012 - ID S2001
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Day 3 Keynote: Not Your Grandfather's Moon Landing
Robert Boehme (Part-Time Scientists), Wes Faler (Part-Time Scientists)
Do not miss the day 3 keynote, featuring Part-Time Scientists Robert Boehme and Wes Faler. Boehme and Faler are part of a team of international scientists and engineers who want to send a rover to the moon before the end of the year 2013. In thi ...Read More

Do not miss the day 3 keynote, featuring Part-Time Scientists Robert Boehme and Wes Faler. Boehme and Faler are part of a team of international scientists and engineers who want to send a rover to the moon before the end of the year 2013. In this presentation, they will discuss their goals, recent accomplishments and milestones, and how GPUs have help in unexpected ways.

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Keywords:
General Interest, GTC Silicon Valley 2012 - ID S3002
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Opening Keynote with Jensen Huang, NVIDIA
Jensen Huang
Do not miss this opening keynote, featuring Jensen Huang, CEO and Co-Founder of NVIDIA and special guests. Hear about what’s next in gpu computing, and preview disruptive technologies and exciting demonstrations from across industries. Jen ...Read More

Do not miss this opening keynote, featuring Jensen Huang, CEO and Co-Founder of NVIDIA and special guests. Hear about what’s next in gpu computing, and preview disruptive technologies and exciting demonstrations from across industries. Jensen Huang co-founded NVIDIA in 1993 and has served since its inception as president, chief executive officer and a member of the board of directors.

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Keywords:
General Interest, GTC China 2011 - ID S10001
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HPC and AI
Presentation
Media
The Evolution of Modern Parallel Computing
Sanford Russell
- NVIDIA
 
Keywords:
HPC and AI, GTC Taiwan 2011 - ID GTCT1101
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General Interest
Presentation
Media
Opening Keynote with Jensen Huang, NVIDIA
Jensen Huang
The opening keynote, features Jensen Huang, CEO and Co-Founder of NVIDIA and special guests. Hear about what''s next in computing and graphics, and preview disruptive technologies and exciting demonstrations from across industries. ...Read More

The opening keynote, features Jensen Huang, CEO and Co-Founder of NVIDIA and special guests. Hear about what''s next in computing and graphics, and preview disruptive technologies and exciting demonstrations from across industries.

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Keywords:
General Interest, GTC Silicon Valley 2010 - ID S091001
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Day 2 Keynote with Dr. Klaus Schluten, University of Illinois at Urbana-Champaign
Klaus Schluten
How does the H1N1 "Swine Flu" virus avoid drugs while attacking our cells? What can we learn about solar energy by studying biological photosynthesis? How do our cells read the genetic code? What comes next in computational biology? Co ...Read More

How does the H1N1 "Swine Flu" virus avoid drugs while attacking our cells? What can we learn about solar energy by studying biological photosynthesis? How do our cells read the genetic code? What comes next in computational biology? Computational biology is approaching a new and exciting frontier: the ability to simulate structures and processes in living cells. Come learn about the "computational microscope," a new research instrument that scientists can use to simulate biomolecules at nearly infinite resolution. The computational microscope complements the most advanced physical microscopes to guide today's biomedical research. In this keynote address, computational biology pioneer Dr. Klaus Schulten of the University of Illinois, Urbana-Champaign, will introduce the computational microscope, showcase the widely used software underlying it, and highlight major discoveries made with the aid of the computational microscope ranging from viewing protein folding, translating the genetic code in cells, and harvesting solar energy in photosynthesis. He will also look towards a future when cell tomography and computing will establish atom-by-atom views of entire life forms.

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Keywords:
General Interest, Life & Material Science, GTC Silicon Valley 2010 - ID S10002
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Closing Keynote with Dr. Sebastien Thrun, Stanford University and Google
Sebastien Thrun
What really causes accidents and congestion on our roadways? How close are we to fully autonomous cars? In his keynote address, Stanford Professor and Google Distinguished Engineer, Dr. Sebastian Thrun, will show how his two autonomous vehicles, ...Read More

What really causes accidents and congestion on our roadways? How close are we to fully autonomous cars? In his keynote address, Stanford Professor and Google Distinguished Engineer, Dr. Sebastian Thrun, will show how his two autonomous vehicles, Stanley (DARPA Grand Challenge winner), and Junior (2nd Place in the DARPA Urban Challenge) demonstrate how close yet how far away we are to fully autonomous cars. Using computer vision combined with lasers, radars, GPS sensors, gyros, accelerometers, and wheel velocity, the vehicle control systems are able to perceive and plan the routes to safely navigate Stanley and Junior through the courses. However, these closed courses are a far cry from everyday driving. Find out what the team will do next to get one step closer to the "holy grail" of computer vision, and a huge leap forward toward the concept of fully autonomous vehicles.

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Keywords:
General Interest, Computer Vision and Machine Vision, Machine Learning & Deep Learning, GTC Silicon Valley 2010 - ID S10003
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Medical Imaging and Radiology
Presentation
Media
Graphcuts with CUDA and Applications in Image Processing
Timo Stich
Graph Cuts is a powerful and popular optimization approach to solve image processing problems such as image segmentation, stereo vision, image restoration and many more. ...Read More
Graph Cuts is a powerful and popular optimization approach to solve image processing problems such as image segmentation, stereo vision, image restoration and many more. In this talk, we present CUDA implementations of the push-relabel algorithm to compute Graph Cuts. Starting from the basic algorithm we discuss its parallel processing properties. Then different optimization strategies are explored and their strengths and weaknesses are evaluated. We conclude by exploring applications of Graph Cuts to solve image processing problems using GPUs.  Back
 
Keywords:
Medical Imaging and Radiology, Medical Imaging and Radiology, GTC Silicon Valley 2009 - ID S91060
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Visualization
Presentation
Media
4D Volume Rendering
Shalini Venkataraman
With advances in image acquisition and numerical simulation techniques, fields ranging from medical imaging to astrophysics are producing data with very large spatial and temporal resolutions. ...Read More
With advances in image acquisition and numerical simulation techniques, fields ranging from medical imaging to astrophysics are producing data with very large spatial and temporal resolutions. Interactive visualization techniques are crucial to understand and isolate features from the resulting large time dependent 4D volumetric data. This presentation explores the various rendering methods such as texture slicing, raycasting in graphics and cuda as well as hybrid approaches showing their promises and pitfalls. It is common for 4D data to exceed the graphics memory capabilities and approaches for efficiently streaming data such as PBO's and CPU/GPU asynchronous modes are explained. We conclude with a discussion on how other related solutions from NVIDIA can be integrated, specifically focusing on 3D Vision stereo and NVScale middleware to harness multiple GPU's for distributed rendering.  Back
 
Keywords:
Visualization, Energy Exploration, Film, Medical Imaging and Radiology, Visualization, GTC Silicon Valley 2009 - ID S91102
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Film
Presentation
Media
Strategies for GPU Acceleration of Common Visual Effects for Broadcast and Post-Production
Boris Yamnitsky, David Yamnitsky
Since 1995 BorisFX has developed image processing and 3D graphics software for Broadcast and Post-Production, with a particular focus on staple visual effects such as 3D Text, Chroma Key, and Film Look. ...Read More
Since 1995 BorisFX has developed image processing and 3D graphics software for Broadcast and Post-Production, with a particular focus on staple visual effects such as 3D Text, Chroma Key, and Film Look. While achieving award-winning quality, these CPU bound effects have lacked the interactivity today''s customers demand. With the advance of GPU hardware from NVIDIA the ability to accelerate these effects has become a reality, and allowed for many previously impractical features to present themselves. In this presentation we will demonstrate how the use of the GPU has benefited our products in terms of performance and features in our creation of GPU-Rendered 3D Text, and discuss the strategies we employed to emphasize the benefits and minimize the drawbacks of the GPU in building the multi-pass Chroma Key and Film Look filters.   Back
 
Keywords:
Film, Film, Visualization, GTC Silicon Valley 2009 - ID S91114
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General Interest
Presentation
Media
Day 2 Keynote with Hanspeter Pfister, Harvard University
Hanspeter Pfister
High-Throughput Science How did the universe start? How is the brain wired? How does matter interact at the quantum level? These are some of the great scientific challenges of our times, and answering them requires bigger scientific instruments, ...Read More

High-Throughput Science How did the universe start? How is the brain wired? How does matter interact at the quantum level? These are some of the great scientific challenges of our times, and answering them requires bigger scientific instruments, increasingly precise imaging equipment and ever-more complex computer simulations. In his keynote address, Harvard professor, researcher and computing visionary Hanspeter Pfister will discuss the computational obstacles scientists face and how commodity high-throughput computing can enable high-throughput science, in which massive data streams are processed and analyzed rapidly -- from the instrument through to the desktop. Finally Professor Pfister will survey several groundbreaking projects at Harvard that leverage GPUs for high- throughput science, ranging from radio astronomy and neuroscience to quantum chemistry and physics.

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Keywords:
General Interest, GTC Silicon Valley 2009 - ID S91422
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Day 3 Keynote with Richard Kerris, Lucasfilm
Richard Kerris
Games and interactive media have long been the beneficiaries of cutting edge GPU technology and it has not gone unnoticed in the world of feature film production. To date the visual effects industry had been a sideline observer of these advances ...Read More

Games and interactive media have long been the beneficiaries of cutting edge GPU technology and it has not gone unnoticed in the world of feature film production. To date the visual effects industry had been a sideline observer of these advances while awaiting technology to reach maturity. At Lucasfilm, research and development has been on-going for some time and this past summer Industrial Light & Magic employed this technology in two of its summer blockbuster films. Lucasfilm CTO, Richard Kerris, will show a brief history of their computer graphics for film, and will then pull back the curtain on how they are now using GPU technology to advance the state of the art in visual effects and provide a glimpse of what's on the horizon for GPU's in future and how it will impact filmmaking.

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Keywords:
General Interest, GTC Silicon Valley 2009 - ID S91423
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AEC Industries
Presentation
Media
An Architectural Design Firm's Journey Through Virtual GPU Technology for Global Collaboration
Jimmy Rotella (CannonDesign), Andrew Schilling (CannonDesign)
Learn the benefits that virtualization provides for an architecture and engineering design firm, along with the journey through the advancements in virtualization technology it took to finally meet the graphics-intensive needs of our design software. ...Read More
Learn the benefits that virtualization provides for an architecture and engineering design firm, along with the journey through the advancements in virtualization technology it took to finally meet the graphics-intensive needs of our design software. We'll share our experiences in how virtualization allows a large company, with over 15 offices and 1,000 people worldwide, to collaborate and work as a single firm. We'll show some cost comparisons with virtualization, along with their management benefits and requirements. We'll also look at the methods we used to set and test metrics specific to our requirements, and follow the results of those metrics through the changes in graphics virtualization technology.  Back
 
Keywords:
AEC Industries, GPU Virtualization, Data Center and Cloud Infrastructure, GTC Silicon Valley 2017 - ID S7174
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Scalable Enterprise Visualization
Adam Glick (Foundry), George Matos (Foundry)
We'll discuss Bunsen, a large-scale visualization framework that prepares and optimizes engineering, architectural, and other CAD and CAM data. Bunsen is a cloud-hosted solution that reads and writes various industry standard file formats (for examp ...Read More
We'll discuss Bunsen, a large-scale visualization framework that prepares and optimizes engineering, architectural, and other CAD and CAM data. Bunsen is a cloud-hosted solution that reads and writes various industry standard file formats (for example, Revit, SOLIDWORKS, Rhino, Maya, Max, Siemens, and Microstation) and provides powerful tools for processing and conversion. It runs on public cloud solutions, such as AWS or Google, or within your own data center or on-prem cloud. All hardware and software are provisioned in the cloud and are usable from any laptop, tablet, or phone with a web browser. Within Bunsen, the user can create sets of reusable rules to process data for visualization and output. You can think of these rules as company standards relating to lighting, materials, colors, and how to reduce object complexity. Possible visualization output platforms include rendering and animation, virtual reality, augmented reality, and real-time game engines, such as Unreal and Unity. Bunsen doesn't mean you change your workflow -- it is a framework to automate, document, and accelerate your existing workflows.  Back
 
Keywords:
AEC Industries, Manufacturing Industries, Rendering and Ray Tracing, GTC Silicon Valley 2017 - ID S7474
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From Cracks to Hard Hats: Focusing on Industrial Computer Vision
Josh Kanner (Smartvid.io, Inc.), Sean TRUE (Smartvid.io, Inc.)
We'll present, in a case study driven presentation, specific examples of how GPU-enabled deep neural networks are powering new methods for analyzing the content of photos and videos from industrial contexts. First, we'll present a collaboration bet ...Read More
We'll present, in a case study driven presentation, specific examples of how GPU-enabled deep neural networks are powering new methods for analyzing the content of photos and videos from industrial contexts. First, we'll present a collaboration between Smartvid.io and Engineering News-Record, the leading publication in the architecture, engineering, and construction vertical. This ongoing initiative leverages computer vision techniques and semantic approaches to help identify and indicate safe and unsafe situations in jobsite photos. Second, we'll present a collaboration with Arup, a London-based engineering firm, on the use of specific classifiers to localize and measure cracks and related defects in infrastructure.  Back
 
Keywords:
AEC Industries, Deep Learning and AI, AI Startup, GTC Silicon Valley 2017 - ID S7575
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Design with Virtual Reality in Architecture, Engineering and Construction
Scott DeWoody (Gensler)
Learn how Gensler is using the latest technology in virtual reality across all aspects of the design process for the AEC industry. We'll cover how VR has added value to the process when using different kinds of VR solutions. Plus we'll t ...Read More

Learn how Gensler is using the latest technology in virtual reality across all aspects of the design process for the AEC industry. We'll cover how VR has added value to the process when using different kinds of VR solutions. Plus we'll talk about some of the challenges Gensler has faced with VR in terms of hardware, software, and workflows. Along with all of this, NVIDIA's latest VR visualization tools are helping with the overall process and realism of our designs.

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Keywords:
AEC Industries, Virtual Reality and Augmented Reality, GTC Silicon Valley 2017 - ID S7614
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AI Application Deployment and Inference
Presentation
Media
Deep Learning Implementers Panel: Field Insights for Accelerating Deep Learning Performance, Productivity and Scale
Tony Paikeday (NVIDIA), Scott Stephenson (Deepgram), Arun Subramaniyan (Baker Hughes, a GE Company), Neil Tenenholtz (MGH and BWH Center for Clinical Data Science)
This customer panel brings together A.I. implementers who have deployed deep learning at scale using NVIDIA DGX Systems. We'll focus on specific technical challenges we faced, solution design considerations, and best practices learned from i ...Read More

This customer panel brings together A.I. implementers who have deployed deep learning at scale using NVIDIA DGX Systems. We'll focus on specific technical challenges we faced, solution design considerations, and best practices learned from implementing our respective solutions. Attendees will gain insights such as: 1) how to set up your deep learning project for success by matching the right hardware and software platform options to your use case and operational needs; 2) how to design your architecture to overcome unnecessary bottlenecks that inhibit scalable training performance; and 3) how to build an end-to-end deep learning workflow that enables productive experimentation, training at scale, and model refinement.

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Keywords:
AI Application Deployment and Inference, AI and DL Business Track (high level), Data Center and Cloud Infrastructure, AI for Business, HPC and Supercomputing, GTC Silicon Valley 2018 - ID S8194
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Deploying Deep Neural Networks as a Service Using TensorRT and NVIDIA-Docker
Alec Gunny (NVIDIA), Prethvi Kashinkunti (NVIDIA)
Learn how you can utilize TensorRT and NVIDIA Docker to quickly configure and deploy a GPU-accelerated inference server and start gaining insights from your trained deep neural network (DNN) models. TensorRT is a high-performance tool for low-latency ...Read More
Learn how you can utilize TensorRT and NVIDIA Docker to quickly configure and deploy a GPU-accelerated inference server and start gaining insights from your trained deep neural network (DNN) models. TensorRT is a high-performance tool for low-latency, high-throughput DNN inference. The latest release of TensorRT introduces a novel, framework-agnostic network definition format called universal framework format, which allows TensorRT to support and optimize DNN models trained in multiple deep learning frameworks. We'll leverage the TensorRT Python API to create a lightweight Python Flask application capable of serving multiple DNN models trained using TensorFlow, PyTorch, and Caffe, and also discuss how to containerize this inference service using NVIDIA Docker for ease of deployment at scale. This session will consist of a lecture, live demos, and detailed instructions.  Back
 
Keywords:
AI Application Deployment and Inference, Tools and Libraries, Data Center and Cloud Infrastructure, GTC Silicon Valley 2018 - ID S8495
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Monte Carlo Methods and Neural Networks
Noah Gamboa (Stanford University)
The average human brain has about 100 billion nerve cells. We therefore investigate the question whether there are algorithms for artificial neural networks that are linear in the number of neurons, while the number of connections incident to a neuro ...Read More
The average human brain has about 100 billion nerve cells. We therefore investigate the question whether there are algorithms for artificial neural networks that are linear in the number of neurons, while the number of connections incident to a neuron is bounded by a constant. We offer two approaches to answer this question: First, we derive an algorithm that quantizes a trained artificial neural network such that the resulting complexity is linear. Second, we demonstrate that training networks, whose connections are determined by uniform sampling can achieve a similar precision as compared to using fully connected layers. Due to sparsity upfront, these networks can be trained much faster. Both approaches are made plausible by relating artificial neural units to Monte Carlo integration. We'll demonstrate the results for classic test datasets.  Back
 
Keywords:
AI Application Deployment and Inference, AI and DL Research, GTC Silicon Valley 2018 - ID S8780
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AI Solutions and Use Cases Up Close (Presented by Inspur Systems)
Dolly Wu (Inspur)
Inspur has been deploying AI solutions with our customers, such as Microsoft, Alibaba, Baidu, BMW, for many years. We will share AI use cases on how we deploy AI at scale and take a close look at the technologies that enable AI deployments.
Inspur has been deploying AI solutions with our customers, such as Microsoft, Alibaba, Baidu, BMW, for many years. We will share AI use cases on how we deploy AI at scale and take a close look at the technologies that enable AI deployments.  Back
 
Keywords:
AI Application Deployment and Inference, AI and DL Research, HPC and AI, GTC Silicon Valley 2018 - ID S8996
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Putting AI to Work in an Enterprise: Deep Learning as a Service (Presented by IBM)
Nick Werstiuk (IBM)
Now that Deep learning has moved out of the lab and into production, how do you provide training environments to all your internal customers working across business units with different requirements and avoid provisioning separate clusters? IBM has a ...Read More
Now that Deep learning has moved out of the lab and into production, how do you provide training environments to all your internal customers working across business units with different requirements and avoid provisioning separate clusters? IBM has applied decades of HPC experience to build a production ready learning stack, including servers accelerated with NVIDIA GPUs, workload and resource management software, ready to use open source frameworks and it's all covered by IBM support. The solution provides a secure multi-tenant environment so multiple data scientists can share a common set of resources, eliminating silos, while running multiple instances of the same or different applications. The deep learning effort is enhanced with end-to-end pipeline support from data ingestion and preparation, through model training and tuning, to inference. In this session, we will explore what an enterprise deep learning environment looks like and provide insights into the unique IBM value for accelerating the use of deep learning across a wide variety of industries.  Back
 
Keywords:
AI Application Deployment and Inference, GTC Silicon Valley 2018 - ID S81049
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GPU-Powered Megacity Scale Transport Management, Municipal Services and Public Safety Solutions
Anton Nazarkin (VisionLabs)
Learn how VisionLabs GPU-powered solutions contribute to creating a safer, smarter Megacity a metropolitan area with a total population in excess of ten million people. We'll do a deep dive into three implemented and ongoing huge scale smart-city ...Read More
Learn how VisionLabs GPU-powered solutions contribute to creating a safer, smarter Megacity a metropolitan area with a total population in excess of ten million people. We'll do a deep dive into three implemented and ongoing huge scale smart-city projects, understand challenges, technical specifics and how GPU computing impacts each of these cases: Face authentication-based immobilizer and driver monitoring systems for municipal service vehicles powered by the NVIDIA Jetson TX2 embedded platform; Megacity scale vehicle traffic analysis and anomalies detection powered by NVIDIA Tesla P40 with over 80 million daily recognition requests; National scale face identification platform for financial services with over 110 million faces in its database. The foundation of all these projects is VisionLabs LUNA a cross-platform object recognition software based on proprietary deep neural networks (DNN) inference framework. To build cost-effective solutions, VisionLabs use know-hows in DNN quantization and acceleration. In terms of accuracy, VisionLabs is recognized as a top three best in the world by National Institute of Standards and Technology's face recognition vendor test, and LFW by University of Massachusetts challenges.  Back
 
Keywords:
AI Application Deployment and Inference, NVIDIA Inception Program, Intelligent Video Analytics and Smart Cities, Deep Learning and AI Frameworks, Computer Vision, GTC Silicon Valley 2018 - ID S8584
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VACnet: Using Deep Learning to Combat Cheating in 'Counter-Strike: Global Offensive'
John McDonald (Valve)
We'll delve into the nuts and bolts of how Valve has utilized deep learning to combat cheating in "Counter-Strike: Global Offensive." We'll cover total system details, from the high-level server architecture to the low-level features fed ...Read More
We'll delve into the nuts and bolts of how Valve has utilized deep learning to combat cheating in "Counter-Strike: Global Offensive." We'll cover total system details, from the high-level server architecture to the low-level features fed into the AI. Deep learning has proven to be very effective at identifying cheating behavior without any client-side instrumentation, making it robust against malicious attack by cheaters and cheat vendors. By retraining regularly, the network continues to evolve, picking up new cheating behaviors within hours of their appearance. As a result of this approach, certain types of cheats have been reduced by a factor of 100.  Back
 
Keywords:
AI Application Deployment and Inference, AI for Gaming, GTC Silicon Valley 2018 - ID S8732
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Autoregressive Wavenet Inference on Volta GPUs
Brian Pharris (NVIDIA)
Autoregressive wavenets have demonstrated extremely high quality real-time speech synthesis results.  However, the compute requirements and tight latency bounds have made them impractical for deployment on traditional CPU-only systems.  In ...Read More
Autoregressive wavenets have demonstrated extremely high quality real-time speech synthesis results.  However, the compute requirements and tight latency bounds have made them impractical for deployment on traditional CPU-only systems.  In this talk we demonstrate that Volta GPUs provide excellent real-time inference performance on these networks, making practical deployments possible.  We discuss several alternative implementation techniques and demonstrate their achieved performance on a V100 GPU.  Back
 
Keywords:
AI Application Deployment and Inference, Speech and Language Processing, GTC Silicon Valley 2018 - ID S8968
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Adopting Artificial Intelligence Technologies in Networking (Presented by Cisco)
Hugo Latapie (Cisco)
This talk will provide an overview of what is happening in the world of artificial intelligence as it relates to networking, IT infrastructure, and IoT technologies. We will broadly cover AI topics ranging from machine learning and deep learning to s ...Read More
This talk will provide an overview of what is happening in the world of artificial intelligence as it relates to networking, IT infrastructure, and IoT technologies. We will broadly cover AI topics ranging from machine learning and deep learning to symbolic AI. Applied AI as well as general AI and their hybrids are all critical in solving many of today's complex long tail problems in real-time. Just as the capabilities, business opportunities, and positive benefits of AI are growing at a seemingly exponential rate so are the security vulnerabilities, failure modes, and potential adverse business impacts. We will discuss new hybrid neural symbolic approaches that promise to address these issues while simultaneously opening the door to powerful systems that dynamically learn and reason at multiple levels of abstraction, from raw data to high-level symbolic reasoning. We will cover use cases and solutions ranging from smart city, transportation, manufacturing, to security and networking.  Back
 
Keywords:
AI Application Deployment and Inference, Advanced AI Learning Techniques (incl. GANs and NTMs), GTC Silicon Valley 2018 - ID S8971
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Accelerating AI Adoption and Impact (Presented by Dell EMC)
Jay Boisseau (Dell)
Attendees will learn and understand why AI techniques are so powerful, why developing and deploying optimal AI solutions is complex, why using AI techniques effectively is still difficult--and what Dell Technologies is doing to remove these difficult ...Read More
Attendees will learn and understand why AI techniques are so powerful, why developing and deploying optimal AI solutions is complex, why using AI techniques effectively is still difficult--and what Dell Technologies is doing to remove these difficulties and bring easier, effective AI to everyone. Dell Technologies includes seven companies with a comprehensive portfolio of technology products, services and solutions for global industry, government, and education markets, and aims to be the leader in designing and delivering the best AI solutions for every customer, of every type and scale. From Dell Precision workstations for developers and Gateways for edge sensors, to Dell EMC GPU-optimized PowerEdge Servers and Ready Solutions for Deep Learning and hybrid cloud offerings, Dell is leveraging its leadership in technology and in enterprise relationships to design a world-class portfolio of AI solutions for diverse customer workloads, requirements and objectives. This presentation will cover AI and deep learning in an enterprise context, including customer challenges and needs, and then discuss Dell AI solutions and strategy to empower people to use AI rapidly and effectively.  Back
 
Keywords:
AI Application Deployment and Inference, GTC Silicon Valley 2018 - ID S81046
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space.ml: Artificial Intelligence Meets Data-Driven Astrophysics
Kevin Schawinski (ETH Zurich), Ce Zhang (ETH Zurich)
We'll present a suite of artificial intelligence applications and computation geared towards increasing our understanding of the universe. The intensive collaboration between astrophysics and computer science has long started since Jim Gray and Alex ...Read More
We'll present a suite of artificial intelligence applications and computation geared towards increasing our understanding of the universe. The intensive collaboration between astrophysics and computer science has long started since Jim Gray and Alex Szalay. Nowadays, astrophysics continues to offer rich datasets, which are ideal for exploration with the latest in AI and computer science in general. We'll present successful projects in our space.ml initiative that try to answer a range of fascinating astrophysics questions. We'll show how we can use generative adversarial networks to go slightly beyond the Nyquist resolution limit in images, and to study the host galaxies of powerful quasars. We demonstrate how we can use transfer learning to identify rare galaxy mergers, and how to use variational autoencoders to forward model the processes in cosmology and galaxy evolution. We'll illustrate how we can use GPUs for compressive sensing to better analyze data from radio arrays, and to model the evolution of black holes over the age of the universe. Attendees will not only get our current answers to these questions but also get a taste of how AI is reshaping science today.  Back
 
Keywords:
AI Application Deployment and Inference, Astronomy and Astrophysics, GTC Silicon Valley 2018 - ID S8667
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Accelerate TensorFlow Inference with New TensorRT Integration
Julie Bernauer (NVIDIA)
TensorFlow is an open source software library for numerical computation using data flow graphs. NVIDIA TensorRT is an inference optimizer and runtime for runtime deployment. TensorRT provides optimizations for deep neural networks and uses reduced pr ...Read More
TensorFlow is an open source software library for numerical computation using data flow graphs. NVIDIA TensorRT is an inference optimizer and runtime for runtime deployment. TensorRT provides optimizations for deep neural networks and uses reduced precision to increase throughput, reduce latency, while maintaining accuracy. Today we announced tighter integration in TensorFlow for TensorRT through with new TensorFlow APIs, sub-graph optimizations and INT8 calibration to automatically leverage Tensor Cores on Volta GPUs. TensorRT delivers 2.5x faster inference throughput compared to inference without TensorRT. In this session, NVIDIA developers will use an example based workflow to show how to use this new capability.  Back
 
Keywords:
AI Application Deployment and Inference, Deep Learning and AI Frameworks, GTC Silicon Valley 2018 - ID S81009
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Deep Learning of Railway Track Faults using GPUs
Nathalie Rauschmayr (CSEM (Swiss Center for Electronics and Microtechnology))
Swiss Federal Railways (SBB) operate a 'diagnosis' train fitted with multiple high-resolution cameras that obtain images of tracks - all while traveling at a speed of 75 mph. Current data processing software conducted in real time on the train prod ...Read More
Swiss Federal Railways (SBB) operate a 'diagnosis' train fitted with multiple high-resolution cameras that obtain images of tracks - all while traveling at a speed of 75 mph. Current data processing software conducted in real time on the train produces  a too high rate of false positives/negatives to the extent that railway experts still need to go on the track to physically inspect anomalies. This is not only very dangerous, but sometimes even impossible and in addition it requires a lot of human labor. We describe how deep learning technologies have been developed to massively improve the automatic detection and classification of railway faults. This is not just a nice-to-have, but rather a must-have in order to ensure the safety of future rail transportation.  Back
 
Keywords:
AI Application Deployment and Inference, Industrial Inspection, GTC Silicon Valley 2018 - ID S8944
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IBM PowerAI: Realizing Business Value with Machine Learning (Presented by IBM)
Adel El-Hallak (IBM)
There is no shortage of hype around AI, but realizing value through machine and deep learning comes with its challenges. IBM PowerAI removes the inhibitors across each stage of a workflow, allowing enterprises to rapidly realize business value with A ...Read More
There is no shortage of hype around AI, but realizing value through machine and deep learning comes with its challenges. IBM PowerAI removes the inhibitors across each stage of a workflow, allowing enterprises to rapidly realize business value with AI.  Back
 
Keywords:
AI Application Deployment and Inference, GTC Silicon Valley 2018 - ID S81048
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NVIDIA GPU Video Technologies and Video Codec SDK: Updates and Roadmap
Abhijit Patait (NVIDIA)
NVIDIA's video SDK is a set of APIs for hardware-accelerated video encoding and decoding using NVIDIA GPUs. We'll provide an overview of the APIs, with particular emphasis on the latest features, such as FFmpeg support of NVIDIA-accelerated transco ...Read More
NVIDIA's video SDK is a set of APIs for hardware-accelerated video encoding and decoding using NVIDIA GPUs. We'll provide an overview of the APIs, with particular emphasis on the latest features, such as FFmpeg support of NVIDIA-accelerated transcoding, quality and performance enhancements. We'll discuss some strategies on efficient usage of GPU video hardware acceleration for use cases such as video inferencing, transcoding, and media archiving.  Back
 
Keywords:
AI Application Deployment and Inference, Video and Image Processing, GTC Silicon Valley 2018 - ID S8601
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Monitoring Honey Bee Health Using TensorRT and Microsoft Cognitive Toolkit
Jacqueline Cenci-McGrody (NVIDIA), Anusua Trivedi (Microsoft)
We'll take a deep dive into honey bee hive health monitoring with NVIDIA's TX2, TensorRT (a high-performance deep learning inference optimizer), Kineticas insight engine running on DGX-1/DGXStaion, and Microsoft Cognitive Toolkit to rapidly o ...Read More
We'll take a deep dive into honey bee hive health monitoring with NVIDIA's TX2, TensorRT (a high-performance deep learning inference optimizer), Kineticas insight engine running on DGX-1/DGXStaion, and Microsoft Cognitive Toolkit to rapidly optimize, validate, and deploy trained neural networks for inference. In recent years, the media has reported that bees seem to be dying at an unprecedented rate. We'll explore how new accelerated analytics technologies and their corresponding compute platforms can deliver game-changing possibilities for innovation as we follow a honey bee farm scientist in California, who agreed to field test this real-time monitoring solution with her beehives.  See first-hand how adaptable and accessible these complex, cutting-edge technologies have become and how we can use intelligent monitoring technologies to help rescue the honey bee in the real-world environmental analytics opportunity.  Back
 
Keywords:
AI Application Deployment and Inference, Deep Learning and AI Frameworks, GTC Silicon Valley 2018 - ID S8508
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Practical Application of Deep Learning in Smart Factory: Visual Inspection System of Semiconductor Laser
Hiroyuki Kusaka (Fujikura Ltd)
Fujikura is pushing forward of implementation of the smart factory with AI and IoT for improving the productivity and production quality. In this presentation, we will present visual inspection system incorporating deep learning in the production pro ...Read More
Fujikura is pushing forward of implementation of the smart factory with AI and IoT for improving the productivity and production quality. In this presentation, we will present visual inspection system incorporating deep learning in the production process of semiconductor lasers. Not only OK/NG classification, but also multiple NG mode classification was performed. The inspection accuracy of 95 % that is equivalent to skilled workers' accuracy was achieved by optimizing the data set and the hyper parameters of a CNN model. The activation map was used for reliability and validity assurance. We will present the difficulty in our practical application in manufacturing industry, such as the small number of some category and small defect/chip size ratio, and also introduce our countermeasures.  Back
 
Keywords:
AI Application Deployment and Inference, Industrial Inspection, GTC Silicon Valley 2018 - ID S8911
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Deep Learning for Heliophysics
Mark Cheung (Lockheed Martin Solar & Astrophysics Laboratory)
NASA's heliophysics division operates a fleet of spacecraft, the so-called Heliophysics System Observatory, to monitor the Sun's activity and how its changes drive space weather in interplanetary space and in the near-Earth environment. We'll pres ...Read More
NASA's heliophysics division operates a fleet of spacecraft, the so-called Heliophysics System Observatory, to monitor the Sun's activity and how its changes drive space weather in interplanetary space and in the near-Earth environment. We'll present case studies of how a number of challenging problems encountered in heliophysics can be tackled using deep learning: spectropolarimetric inversions for measuring the magnetic field on the solar surface, and mega-Kelvin thermometry of the Sun's corona by using a deep neural network to solve a compressed sensing problem. These low-cost solutions make possible new concepts for deep space missions for space weather monitoring. Some of the work in this presentation was made possible by NASA's Frontier Development Lab, a public-private partnership between the agency and industry partners (including the SETI Institute, NVIDIA, IBM, Intel, kx & Lockheed Martin), whose mission is to use artificial intelligence to tackle problems related to planetary defense and heliophysics.  Back
 
Keywords:
AI Application Deployment and Inference, Accelerated Analytics, Astronomy and Astrophysics, GTC Silicon Valley 2018 - ID S8222
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How AI Technology Lifts the Ads Business in JD.com
Juyan Song (NVIDIA), YAN YAN (JD.com)
Deep learning and reinforcement learning are widely used in ads products of JD.com, e.g. ranking model in recommender systems, bidding model in ad exchange business and automatic ads review systems. These technologies have brought great benefits to J ...Read More
Deep learning and reinforcement learning are widely used in ads products of JD.com, e.g. ranking model in recommender systems, bidding model in ad exchange business and automatic ads review systems. These technologies have brought great benefits to JD.com and all of them are built on Nvidia GPUs.  Back
 
Keywords:
AI Application Deployment and Inference, Consumer Engagement and Personalization, GTC Silicon Valley 2018 - ID S81016
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A Map of Knowledge: Using Behavioral Data in Higher-Ed to Surface Novel Semantic Structure and Personalized Guidance
Zachary Pardos (UC Berkeley)
Personalized learning has been a promising but often elusive ideal sought after in education. We'll demonstrate the progress made with two concrete examples of personalized learning supports implemented at scale in a massive open online course (MOOC ...Read More
Personalized learning has been a promising but often elusive ideal sought after in education. We'll demonstrate the progress made with two concrete examples of personalized learning supports implemented at scale in a massive open online course (MOOC) and on the UC Berkeley campus in a collaboration with the Office of the Registrar. Both approaches employ long short-term memory to leverage a collaborative signal out of millions of historic learner actions. In the case of the MOOC, the next page a learner is expected to spend considerable time on is predicted and offered as a real-time suggestion. At the university, we consider sequences of millions of historic enrollments over the past eight years. These sequences of course identifiers, when modeled with representation learning approaches most commonly applied to natural language, reveal a tremendous degree of semantic relational information about the courses which can be visualized, reasoned about, and surfaced to students. Our course information platform uses this automatically inferred semantic information to help students navigate the university's offerings and provides personalized course suggestions based on topic preference.  Back
 
Keywords:
AI Application Deployment and Inference, Consumer Engagement and Personalization, AI and DL Research, GTC Silicon Valley 2018 - ID S8597
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Pioneering AI for All
Danny Lange (Unity)
Businesses of all sizes are increasingly recognizing the potential value of AI, but few are sure how to prepare for the transformational change it is sure to bring to their organizations. Danny Lange rolled out company-wide AI platforms at Uber ...Read More

Businesses of all sizes are increasingly recognizing the potential value of AI, but few are sure how to prepare for the transformational change it is sure to bring to their organizations. Danny Lange rolled out company-wide AI platforms at Uber and Amazon; now, through Unity Technologies, he's making AI available to the rest of us. He'll also share his thoughts for the most exciting advances that AI will bring over the next year. His insights will help you understand the true potential of AI, regardless of your role or industry.

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Keywords:
AI Application Deployment and Inference, Advanced AI Learning Techniques (incl. GANs and NTMs), AI and DL Business Track (high level), AI for Business, GTC Silicon Valley 2018 - ID S8729
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Low-Latency GPU Accelerated Inferencing with TensorRT
Prethvi Kashinkunti (NVIDIA)
Come learn how you can optimize the deployment of your trained neural networks using the GPU-accelerated inferencing library called TensorRT. TensorRT is a high-performance tool for low-latency, high-throughput deep neural network (DNN) inference tha ...Read More
Come learn how you can optimize the deployment of your trained neural networks using the GPU-accelerated inferencing library called TensorRT. TensorRT is a high-performance tool for low-latency, high-throughput deep neural network (DNN) inference that runs on NVIDIA GPUs. The latest release of TensorRT introduces a novel, framework-agnostic network definition format called universal framework format, allowing TensorRT to support and optimize DNN models trained in multiple deep learning frameworks like Caffe and TensorFlow. It also provides the capability to run inference at reduced precision, giving developers the ability to take advantage of new GPU hardware features like the Volta Tensor Core architecture. This session will be a combination of lecture and live demos.  Back
 
Keywords:
AI Application Deployment and Inference, Tools and Libraries, Performance Optimization, Data Center and Cloud Infrastructure, GTC Silicon Valley 2018 - ID S8496
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Intelligent Talent Management - AI Drives Transformation
Arjun Pratap (AVR EdGE Networks Pvt. Ltd.)
Artificial intelligence helps you hire faster and smarter. It also helps you determine your career path, learning, and development. Wondering how? AI platforms have a brain that reads, understands, and analyzes just as human beings do. They can read ...Read More
Artificial intelligence helps you hire faster and smarter. It also helps you determine your career path, learning, and development. Wondering how? AI platforms have a brain that reads, understands, and analyzes just as human beings do. They can read thousands and millions of resumes, JDs, career progressions, and learning content in a matter of seconds. This equips them with intelligence creating a neural network of skills, demographics, industries, occupations, and courses/certifications. This acts as the central intelligence powering search and match algorithms to find accurate matches to job demands in a few seconds. The NLP layer helps understand intent, for example, it differentiates between 'Worked with a PM' and 'Worked as a PM' to determine that the former could work collaboratively and the latter could drive projects. AI platforms mimic a recruiter or hiring manager's brain to find that right match. What takes HR 20-30 days is done in a few seconds by an AI platform. It helps HR leaders in workforce planning by forecasting what skills and domains to invest, maintain, or upgrade in their organizations, which could be a game changer especially for people-centric organizations.  Back
 
Keywords:
AI Application Deployment and Inference, Accelerated Analytics, AI and DL Research, AI and DL Business Track (high level), GTC Silicon Valley 2018 - ID S8303
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Deploying, Profiling, and Optimizing Distributed TensorFlow in Production with GPUs
Chris Fregly (PipelineAI)
Using the latest advancements from TensorFlow including the Accelerated Linear Algebra (XLA) Framework, JIT/AOT Compiler, and Graph Transform Tool, we'll demonstrate how to optimize, profile, and deploy TensorFlow models in GPU-based production envi ...Read More
Using the latest advancements from TensorFlow including the Accelerated Linear Algebra (XLA) Framework, JIT/AOT Compiler, and Graph Transform Tool, we'll demonstrate how to optimize, profile, and deploy TensorFlow models in GPU-based production environments. We'll cover many demos based on open source tools. You can completely reproduce all demos through Docker on your own GPU cluster. See http://pipeline.ai for links to the GitHub Repo.  Back
 
Keywords:
AI Application Deployment and Inference, NVIDIA Inception Program, Deep Learning and AI Frameworks, GTC Silicon Valley 2018 - ID S8621
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Latest Tools and Techniques for Training and Deploying Deep Neural Networks in Educational Environments
Joseph Bungo (NVIDIA), Dmytro Lituiev (UC Berkeley and UCSF), Craig Morioka (UCLA)
Craig Morioka, UCLA Adjunct Associate Professor of Radiological Sciences, and Dima Lituiev, Postdoctoral Scholar at the University of California San Francisco, Institute for Computational Health Sciences, will discuss how they empower their fellow fa ...Read More
Craig Morioka, UCLA Adjunct Associate Professor of Radiological Sciences, and Dima Lituiev, Postdoctoral Scholar at the University of California San Francisco, Institute for Computational Health Sciences, will discuss how they empower their fellow faculty, staff, and students with the latest techniques in training and deploying deep neural networks through NVIDIAs Deep Learning Institute (DLI) University Ambassador Program - a new AI and Deep Learning education enablement program for universities. This will include a dive into the benefits of an online learning platform, which uses GPUs in the cloud, by stepping through the DLIs online Image Segmentation and Radiomics labs. The Image Segmentation lab leverages an example from medical image analysis where it is often important to separate pixels corresponding to different types of tissue or cells for the purposes of diagnostics and treatment planning. Dima uses image segmentation in his research to facilitate diagnostics of kidney rejection by analyzing histological slides from patients with kidney transplants. We will explore how the Tensorflow code is structured and how the Tensorboard tool can be used to visualize structure and training dynamics of segmentation models. The focus of the Radiomics lab is detection of the 1p19q co-deletion biomarker using deep learning - specifically convolutional neural networks using the Keras and TensorFlow computing frameworks. Attendees will also learn how they can apply to become a DLI University Ambassador and bring the latest in Deep Learning and AI education to their academic communities.    Back
 
Keywords:
AI Application Deployment and Inference, Deep Learning and AI Frameworks, AI and DL Business Track (high level), GTC Silicon Valley 2018 - ID S8823
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Protecting Pulsed High-Power Lasers with Real-Time Image Classification
Jeffrey Kelling (Helmholtz-Zentrum Dresden - Rossendorf)
Learn how to combine computer vision techniques and deep learning to improve the sensitivity of a real-time, GPU-powered safety system. In petawatt laser systems, firing at 10 Hz, suddenly appearing scatterers can damage components. Spreading of dama ...Read More
Learn how to combine computer vision techniques and deep learning to improve the sensitivity of a real-time, GPU-powered safety system. In petawatt laser systems, firing at 10 Hz, suddenly appearing scatterers can damage components. Spreading of damage can be avoided by suspending operation immediately on occurrence of such an event. We'll present our approach for the automatic detection of critical failure states from intensity profiles of the laser beam. By incorporating quick feature detection and learned heuristics for feature classification, both real-time constraints and limited available training data are accommodated. Localization of triggering feature is crucial for when the problem is located in non-sensitive sections and will not be removed from the beam in production.  Back
 
Keywords:
AI Application Deployment and Inference, Advanced AI Learning Techniques (incl. GANs and NTMs), Computer Vision, GTC Silicon Valley 2018 - ID S8330
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Driver Drowsiness Detection for ADAS
Sidharth Varier (NVIDIA)
We'll present an in-car ADAS technology to detect drowsy driving. This technique can be used to alert and awaken the driver, or take corrective actions if required. We employ a CNN-based approach for this technique, which is trained on a mix of synt ...Read More
We'll present an in-car ADAS technology to detect drowsy driving. This technique can be used to alert and awaken the driver, or take corrective actions if required. We employ a CNN-based approach for this technique, which is trained on a mix of synthetic and real images. We'll cover the details of the detection system pipeline and the synthetic dataset generation. We'll also show a demonstration of the detection system in action.  Back
 
Keywords:
AI Application Deployment and Inference, Autonomous Vehicles, GTC Silicon Valley 2018 - ID S8399
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Deep Learning Demystified
William Ramey (NVIDIA)
What is Deep Learning? In what fields is it useful? How does it relate to artificial intelligence? We'll discuss  deep learning and why this powerful new technology is getting so much attention, learn how deep neural networks are traine ...Read More

What is Deep Learning? In what fields is it useful? How does it relate to artificial intelligence? We'll discuss  deep learning and why this powerful new technology is getting so much attention, learn how deep neural networks are trained to perform tasks with super-human accuracy, and the challenges organizations face in adopting this new approach. We'll also cover some of the best practices, software, hardware, and training resources that many organizations are using to overcome these challenges and deliver breakthrough results.

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Keywords:
AI Application Deployment and Inference, Deep Learning and AI Frameworks, Deep Learning and AI, GTC Silicon Valley 2018 - ID S8669
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CatBoost: Fast Open-Source Gradient Boosting Library For GPU
Vasily Ershov (Yandex)
Learn how to use GPUs to accelerate gradient boosting on decision trees. We'll discuss CUDA implementation of CatBoost an open-source library that successfully handles categorical features and shows better quality compared to other open-source gra ...Read More
Learn how to use GPUs to accelerate gradient boosting on decision trees. We'll discuss CUDA implementation of CatBoost an open-source library that successfully handles categorical features and shows better quality compared to other open-source gradient boosted decision trees libraries. We'll provide a brief overview of problems which could be solved with CatBoost. Then, we'll discuss challenges and key optimizations in the most significant computation blocks. We'll describe how one can efficiently build histograms in shared memory to construct decision trees and how to avoid atomic operation during this step. We'll provide benchmarks that shows that our GPU implementation is five to 40 times faster compared to Intel server CPUs. We'll also provide performance comparison against GPU implementations of gradient boosting in other open-source libraries.  Back
 
Keywords:
AI Application Deployment and Inference, Tools and Libraries, HPC and AI, GTC Silicon Valley 2018 - ID S8393
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Leveraging GPUs for Bayesian Inference
Alec Gunny (NVIDIA), Alex Kozlov (NVIDIA)
We'll present results on speeding up Bayesian inference in NVIDIA DGX-1 server for medical diagnostics. Bayesian inference is an AI technique to reason under uncertainty that is computationally and data intensive. We'll discuss the implications for ...Read More
We'll present results on speeding up Bayesian inference in NVIDIA DGX-1 server for medical diagnostics. Bayesian inference is an AI technique to reason under uncertainty that is computationally and data intensive. We'll discuss the implications for both inference and training of Bayesian networks.  Back
 
Keywords:
AI Application Deployment and Inference, Accelerated Analytics, GTC Silicon Valley 2018 - ID S8488
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Prototyping Vision-Based Classifiers in Constrained Environments
Ted Hromadka (Integrity Applications Incorporated)
SOFWERX developed a vision-based classifier using commodity hardware and machine learning libraries to satisfy an urgent high-level requirement. To track the usage of tank ammunition, the team had to address challenges involving unavailable training ...Read More
SOFWERX developed a vision-based classifier using commodity hardware and machine learning libraries to satisfy an urgent high-level requirement. To track the usage of tank ammunition, the team had to address challenges involving unavailable training data, varying spatial orientations, and limited power consumption. To resolve these challenges, SOFWERX generated an augmented dataset using synthetic models, implemented spatial transformers, and experimented with different hardware/software optimizations.  Back
 
Keywords:
AI Application Deployment and Inference, Performance Optimization, GTC Silicon Valley 2018 - ID S8193
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Enabling Deep Learning Applications in Radio Frequency Systems
John Ferguson (Deepwave Digital)
Artificial intelligence has made great strides in many technology sectors, however, it has yet to impact the design and applications of radio frequency (RF) and wireless systems. This is primarily due to the industry''s preference towards field progr ...Read More
Artificial intelligence has made great strides in many technology sectors, however, it has yet to impact the design and applications of radio frequency (RF) and wireless systems. This is primarily due to the industry''s preference towards field programmable gate array (FPGA) systems. Conversely, the deep learning revolution has been fueled by GPUs and the ease with which they may be programmed for highly parallel computations. The next generation RF and wireless technology will require wide-band systems capable of real-time machine learning with GPUs. Working with strategic partners, we''ve designed a software configurable wide-band RF transceiver system capable of performing real-time signal processing and machine learning with a Jetson TX2. We discuss system performance, collection of RF training data, and the software used by the community to create custom applications. Additionally, we''ll present data demonstrating applications in the field of RF machine learning and deep learning.  Back
 
Keywords:
AI Application Deployment and Inference, NVIDIA Inception Program, Cyber Security, IoT, Robotics & Autonomous Machines, GTC Silicon Valley 2018 - ID S8375
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Performance Optimization for Deep Image Matting in Photoshop
Christopher Hebert (NVIDIA), Betty Leong (Adobe Systems), Salil Tambe (Adobe Systems)
Learn how a research paper from Adobe Research Labs makes it into a real customer product like Photoshop. We attempted to solve a number of challenging issues about applying the technology to real-world use cases, including large model size, heavy me ...Read More
Learn how a research paper from Adobe Research Labs makes it into a real customer product like Photoshop. We attempted to solve a number of challenging issues about applying the technology to real-world use cases, including large model size, heavy memory consumption, and slow runtime performance.  Back
 
Keywords:
AI Application Deployment and Inference, GTC Silicon Valley 2018 - ID S8550
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Optimizing NMT with TensorRT
Micah Villmow (NVIDIA)
OpenNMT is an open source neural machine translation and neural machine sequencing model. Using Volta Tensor Cores and TensorRT, we''re able to improve performance by 100 times over CPU implementation. We''ll discuss OpenNMT and how we implement it v ...Read More
OpenNMT is an open source neural machine translation and neural machine sequencing model. Using Volta Tensor Cores and TensorRT, we''re able to improve performance by 100 times over CPU implementation. We''ll discuss OpenNMT and how we implement it via TensorRT. We''ll show how by using our plugin interface and new TensorRT features, we''re able to implement this network at high performance.  Back
 
Keywords:
AI Application Deployment and Inference, Advanced AI Learning Techniques (incl. GANs and NTMs), GTC Silicon Valley 2018 - ID S8822
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Breaking the Barriers to AI-Scale in the Enterprise
Charles Boyle (NVIDIA)
Organizations everywhere want to AI-infuse every aspect of their business, but need a platform that delivers the scale and flexibility to fit both IT operational constraints, as well as workload performance demanded by data scientists. Attend this se ...Read More
Organizations everywhere want to AI-infuse every aspect of their business, but need a platform that delivers the scale and flexibility to fit both IT operational constraints, as well as workload performance demanded by data scientists. Attend this session to get see the latest advancements in scaling in GPU servers and deep learning software, and hear how the latest solutions from NVIDIA solve your biggest AI platform challenges  Back
 
Keywords:
AI Application Deployment and Inference, Data Center and Cloud Infrastructure, AI and DL Research, GTC Silicon Valley 2018 - ID S8196
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Continuous Delivery of AI Applications
Asif Khan (Amazon)
Deep learning systems are usually developed by data scientists, who are good at mathematics and computer science. But to deploy and operationalize these models for broader use, you need the devops mindset and tools. We''ll show you how to connect the ...Read More
Deep learning systems are usually developed by data scientists, who are good at mathematics and computer science. But to deploy and operationalize these models for broader use, you need the devops mindset and tools. We''ll show you how to connect the workflow between the data scientists and devops. We''ll also explore basic continuous integration and delivery concepts and how they can be applied to deep learning models. Using a number of AWS services, we''ll showcase how you can take the output of a deep learning model and deploy it to perform predictions in real time with low latency and high availability. In particular, we''ll showcase the ease of deploying DL to predict functions using Apache MXNet (a deep learning library), Amazon ECS, Amazon S3, and Amazon ECR, Amazon developer tools, and AWS CloudFormation.  Back
 
Keywords:
AI Application Deployment and Inference, GTC Silicon Valley 2018 - ID S8173
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Defect Inspection from Scratch to Production
Kuan-Liang (Andrew) Liu (NVIDIA), Sheng-Ting Shen (NVIDIA)
In order to fulfill customer''s requirement, companies have to guarantee the quality of delivered products, which can often be achieved only by manually inspection of the finished product. Since human-based defect inspection and classification are ti ...Read More
In order to fulfill customer''s requirement, companies have to guarantee the quality of delivered products, which can often be achieved only by manually inspection of the finished product. Since human-based defect inspection and classification are time-consuming and the results vary by individuals, automatic defect detection and classification has the potential to reduce the cost of quality assurance significantly. In this talk, we will demonstrate how to utilize deep learning algorithms, i.e., Fully Convolutional Neural Network to build a general defect inspection and classification model. We will also share experiences on how to effectively collect labelling data, deal with imbalance data, and also how to optimize the model in terms of latency and throughput with TensorRT before deploy the model to the production line.  Back
 
Keywords:
AI Application Deployment and Inference, Industrial Inspection, IoT, Robotics & Drones, Robotics & Autonomous Machines, GTC Silicon Valley 2018 - ID S8682
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Identifying Defect Patterns in Hard Disk Drive Magnetic Media Manufacturing Processes Using Real and Synthetic Data
Nicholas Propes (Seagate Technology)
Learn how synthetic data can be used to develop traditional and Convolutional Neural Network (CNN) image segmentation models when labelled training data is limited. We will describe hard drive media defect patterns and how they relate to problems i ...Read More
Learn how synthetic data can be used to develop traditional and Convolutional Neural Network (CNN) image segmentation models when labelled training data is limited. We will describe hard drive media defect patterns and how they relate to problems in the manufacturing line, show why CNN models were chosen for some defect patterns, and how the CNN models were trained using both synthetic and real data. Different architectures using CNNs were explored and the resulting benefits and drawbacks are presented.  Back
 
Keywords:
AI Application Deployment and Inference, Industrial Inspection, IoT, Robotics & Drones, Robotics & Autonomous Machines, GTC Silicon Valley 2018 - ID S8415
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Using AI for Interactive Applications
Ahmed Zakaria (Microsoft)
Machine learning has revolutionized many important fields, ranging from computer vision and natural language processing to healthcare and robotics. In this session, we will discuss how developers can embrace machine learning methods for graphics and ...Read More
Machine learning has revolutionized many important fields, ranging from computer vision and natural language processing to healthcare and robotics. In this session, we will discuss how developers can embrace machine learning methods for graphics and gaming. We''ll cover both gaming use cases and general applications of machine learning as well as how to best leverage recent GPU hardware for machine learning workloads.  Back
 
Keywords:
AI Application Deployment and Inference, Graphics and AI, AI for Gaming, Rendering and Ray Tracing, GTC Silicon Valley 2018 - ID S8957
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Anomaly Detection on Vehicle CAN BUS
Gorkem Batmaz (NVIDIA), Ildiko Pete (NVIDIA)
We''ll discuss anomaly detection on vehicle CAN BUS. We developed a novel solution for neural networks to detect anomalies in CAN data. Due to the inherent characteristics of controller area (CAN) networks, such as lack of authentication and followin ...Read More
We''ll discuss anomaly detection on vehicle CAN BUS. We developed a novel solution for neural networks to detect anomalies in CAN data. Due to the inherent characteristics of controller area (CAN) networks, such as lack of authentication and following a broadcast routing scheme, devices connected to a CAN network are exposed to a broad range of cyberattacks. Our work aims to alleviate this problem by providing an anomaly detection mechanism, that is, identifying deviations from normal network traffic, to enhance the security of CAN networks. This invention is leveraged as one of the intrusion detection methods in a broader NVIDIA embedded software security system deployed in automotive platforms. In this specific application, the embedded system is a car computer -- an embedded system deployed in modern vehicles. Typical examples: infotainment systems, ADAS units, dashboards, head units. The vulnerable endpoints are all the peripherals connected to the computer. Typical examples: sensors, cameras, media devices, local and wide area communication interfaces and devices (for example, WiFi, BT, cellular), specific car network interfaces and devices.  Back
 
Keywords:
AI Application Deployment and Inference, Deep Learning and AI Frameworks, Cyber Security, Autonomous Vehicles, GTC Silicon Valley 2018 - ID S8347
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Highly-Efficient Caching with Tiling & Chaining in CNN
Yao Yao (NVIDIA)
Learn how to achieve 100% R/W cache hit rate for most intermediate tensors in CNN and over 80% typical DRAM traffic saving, with general applicability to a limited cache size and large tensors. The high-throughput NVIDIA Tensor Core and DLA demand hi ...Read More
Learn how to achieve 100% R/W cache hit rate for most intermediate tensors in CNN and over 80% typical DRAM traffic saving, with general applicability to a limited cache size and large tensors. The high-throughput NVIDIA Tensor Core and DLA demand high memory traffic. Chaining of consecutive layers in CNN can save DRAM traffic by reusing intermediate tensors in cache. This strategy is effective only with small tensors and a large cache. In this work, we slice tensors into small tiles (with halo) and chain these tiles so the requirement for perfect caching can always be fulfilled. Our implementation of this approach is proven to be very effective in saving DRAM traffic. This work allows us to solve the memory bandwidth issue of CNN with a relatively small but high-bandwidth cache.  Back
 
Keywords:
AI Application Deployment and Inference, Performance Optimization, GTC Silicon Valley 2018 - ID S8299
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Scalable, Responsive, and Cost-Effective Object Detection Service for Web-Scale Images
Yan Wang (Microsoft)
We''ll introduce how Bing built a scalable, responsive, and economical object detection API based on NVIDIA GPUs and Azure cloud platforms. Object detection is an important image understanding technique as the entry point or dispatcher to guide users ...Read More
We''ll introduce how Bing built a scalable, responsive, and economical object detection API based on NVIDIA GPUs and Azure cloud platforms. Object detection is an important image understanding technique as the entry point or dispatcher to guide users to more specific scenarios. However, it is very challenging to provide object detection services on web-scale images because it is intrinsically a compute-intensive task and thus resource demanding. We''ll also introduce how to use NVIDIA''s CUDA profiling toolchain and cuDNN to make the system even more cost-effective. The system currently supports billion-level traffic, covering Bing''s entire index.  Back
 
Keywords:
AI Application Deployment and Inference, Performance Optimization, GTC Silicon Valley 2018 - ID S8620
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Revisiting the TurboCharged Test Toolbox: VR, Robotics, and More DL
Martina Sourada (NVIDIA)
Last year, we began to see promising results of applying Deep Learning in an unexpected space: hardware QA. Fast forward +365, and the efforts have been to expand on what we''ve learned, push the technology broader and into other areas that will ulti ...Read More
Last year, we began to see promising results of applying Deep Learning in an unexpected space: hardware QA. Fast forward +365, and the efforts have been to expand on what we''ve learned, push the technology broader and into other areas that will ultimately aid in our greatest challenge: testing at scale. In this session we will highlight a new piece of the problem we are tackling: VR. We will introduce methodologies for not only addressing the unique problems that VR testing presents, but will also showcase some of the other test process areas where we are applying other Deep Learning models to gain efficiency in our overall production pipeline. From using DL on our bug mining to create a quicker path from tester to developer and back, to analysis on end user issues as a method for task automation, explore how we are enabling speed, accuracy and efficiency.  Back
 
Keywords:
AI Application Deployment and Inference, Virtual Reality and Augmented Reality, Tools and Libraries, Graphics and AI, AI for Gaming, GTC Silicon Valley 2018 - ID S8262
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Building Seeing AI : The Talking Camera App for the Blind
Anirudh Koul (Microsoft)
We''ll detail the journey of building Seeing AI, an app from Microsoft AI & Research that narrates the world around you. Designed for the blind and low-vision community, this research project harnesses the power of AI to describe people, text, an ...Read More
We''ll detail the journey of building Seeing AI, an app from Microsoft AI & Research that narrates the world around you. Designed for the blind and low-vision community, this research project harnesses the power of AI to describe people, text, and objects. Seeing AI leverages object classification, detection, image captioning, and more, with several running on the device in real time at more than 15 frames per second. We''ll go over the learnings, challenges, hits, and misses we encountered while developing the application.  Back
 
Keywords:
AI Application Deployment and Inference, Computer Vision, GTC Silicon Valley 2018 - ID S8598
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Deep Learning Infrastructure for Autonomous Vehicles
Pradeep Gupta (NVIDIA)
We''ll introduce deep learning infrastructure for building and maintaining autonomous vehicles, including techniques for managing the lifecycle of deep learning models, from definition, training and deployment to reloading and life-long ...Read More

We''ll introduce deep learning infrastructure for building and maintaining autonomous vehicles, including techniques for managing the lifecycle of deep learning models, from definition, training and deployment to reloading and life-long learning. DNN autocurates and pre-labels data in the loop. Given data, it finds the best run-time optimized deep learning models. Training scales with data size beyond multi-nodes. With these methodologies, one takes only data from the application and feeds DL predictors to it. This infrastructure is divided into multiple tiers and is modular, with each of the modules containerized to lower infrastructures like GPU-based cloud infrastructure.

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Keywords:
AI Application Deployment and Inference, Data Center and Cloud Infrastructure, Autonomous Vehicles, Autonomous Machines, GTC Silicon Valley 2018 - ID S8531
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Deploying Machine Learning on the Oilfield: From the Labs to the Edge
Loryne Bissuel-Beauvais (Schneider Electric), Bartosz Boguslawski (Schneider Electric), Matthieu Boujonnier (Schneider Electric)
Deploying machine learning-based predictive models to the oil field is quite challenging. They are remote, hazardous, and have spotty connectivity to the cloud. The world of operationalizing a model is very different than the perfect lab environment ...Read More
Deploying machine learning-based predictive models to the oil field is quite challenging. They are remote, hazardous, and have spotty connectivity to the cloud. The world of operationalizing a model is very different than the perfect lab environment where the models are born. We'll detail the requirements of our oil and gas customers and how we were able to meet those requirements such that we could deploy a new generation of analytics with a complete software engineering discipline and mentality around it by taking advantage of the Microsoft IoT Edge platform. This is currently a pilot project under way and, due to the engineering principals in place, we are able to complete a loop from the field to the lab and back again.  Back
 
Keywords:
AI Application Deployment and Inference, IoT, Robotics & Drones, Robotics & Autonomous Machines, GTC Silicon Valley 2018 - ID S8714
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Digital Twin for the Railway Network
Dattaraj Rao (General Electric)
We describes concept of Digital Twin with respect to the Railway Network. Railroad customers across the world manage thousands of miles of Track infrastructure that consists of the Rails, Ballast, Ties, Bridges, Tunnels, Wayside equipment, etc. This ...Read More
We describes concept of Digital Twin with respect to the Railway Network. Railroad customers across the world manage thousands of miles of Track infrastructure that consists of the Rails, Ballast, Ties, Bridges, Tunnels, Wayside equipment, etc. This talk demonstrates a new approach to Track infrastructure monitoring that GE is piloting for customers using the concept of Digital Twin for network. Using an offline GPU infrastructure Deep Learning models are created and trained on large volumes of video data to learn the state of healthy Track and predict anomalies. During the talk, real customer use-case videos will be shown that demonstrate Analytics on videos from Locomotive-mounted cameras with Deep Learning models to calculate health index and display on a map for driving Maintenance decisions.  Back
 
Keywords:
AI Application Deployment and Inference, Computer Vision, GTC Silicon Valley 2018 - ID S8614
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How Deep Learning Could Predict Weather Events
Sa-Kwang Song (Korea Institute of Science and Technology)
How do meteorologists predict weather or weather events such as hurricanes, typhoons, and heavy rain? Predicting weather events were done based on supercomputer (HPC) simulations using numerical models such as WRF, UM, and MPAS. But recently, many de ...Read More
How do meteorologists predict weather or weather events such as hurricanes, typhoons, and heavy rain? Predicting weather events were done based on supercomputer (HPC) simulations using numerical models such as WRF, UM, and MPAS. But recently, many deep learning-based researches have been showing various kinds of outstanding results. We'll introduce several case studies related to meteorological researches. We'll also describe how the meteorological tasks are different from general deep learning tasks, their detailed approaches, and their input data such as weather radar images and satellite images. We'll also cover typhoon detection and tracking, rainfall amount prediction, forecasting future cloud figure, and more.  Back
 
Keywords:
AI Application Deployment and Inference, Climate, Weather, Ocean Modeling, Computer Vision, HPC and AI, GTC Silicon Valley 2018 - ID S8816
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Visual Search at eBay
Fan Yang (eBay)
We'll share information and lessons learned from developing a scalable visual search engine to handle a massive volatile inventory like eBay. We'll describe how eBay data is challenging for visual search, how to leverage a single deep neural networ ...Read More
We'll share information and lessons learned from developing a scalable visual search engine to handle a massive volatile inventory like eBay. We'll describe how eBay data is challenging for visual search, how to leverage a single deep neural network to perform multiple tasks efficiently, how to deploy our solution in a distributed cloud infrastructure, and which optimizations we have made for a trade-off between relevance and latency. We'll give examples and insights to benefit computer vision practitioners in the industry who intend to build up visual search engines from scratch.  Back
 
Keywords:
AI Application Deployment and Inference, Data Center and Cloud Infrastructure, Computer Vision, GTC Silicon Valley 2018 - ID S8766
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The Long Road to Model Deployment or how to make a good model great!
Gregory Heinrich (NVIDIA)
In this talk we will cover the essential building blocks of the AI platform Nvidia engineers are using to build a world-class automotive perception stack. Through a computer vision application example, we will see how to improve a baseline model to p ...Read More
In this talk we will cover the essential building blocks of the AI platform Nvidia engineers are using to build a world-class automotive perception stack. Through a computer vision application example, we will see how to improve a baseline model to produce better, faster predictions. The talk will focus on: - hyper-parameter optimization, - model complexity reduction (pruning), - target platform optimizations (TensorRT integration), - automation of complex workflows  Back
 
Keywords:
AI Application Deployment and Inference, Deep Learning and AI Frameworks, GTC Silicon Valley 2018 - ID S8633
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Containerizing Deep Learning with Singularity
Nishanth Dandapanthula (Dell EMC)
We'll talk about how to use Singularity to containerize deep learning applications. We'll provide compelling reasons to choose Singularity over Docker. We'll cover deep learning frameworks, including TensorFlow, NV-Caffe, MXNet, and others. We'll ...Read More
We'll talk about how to use Singularity to containerize deep learning applications. We'll provide compelling reasons to choose Singularity over Docker. We'll cover deep learning frameworks, including TensorFlow, NV-Caffe, MXNet, and others. We'll present the current challenges and workarounds when using Singularity in a HPC cluster. We'll compare the performance of Singularity to bare-metal systems.  Back
 
Keywords:
AI Application Deployment and Inference, HPC and AI, GTC Silicon Valley 2018 - ID S8368
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ANI-AL: Universal Deep Learning Potentials for Organic Molecules and Materials
Justin Smith (University of Florida)
We'll introduce ANI-AL molecular potentials, which are deep learning based potential energy functions for the fast and accurate prediction of quantum mechanical energies and forces of molecular systems. Thanks to GPU acceleration of training and inf ...Read More
We'll introduce ANI-AL molecular potentials, which are deep learning based potential energy functions for the fast and accurate prediction of quantum mechanical energies and forces of molecular systems. Thanks to GPU acceleration of training and inference, we successfully implement an automated sampling method that borrows techniques from active learning to automatically drive the systematic improvement of ANI-AL potentials. We'll also present results from applications of the ANI-AL potential in various problems relating to computational chemistry, such as molecular structure optimization, reaction path prediction, vibrational frequency calculation, and molecular dynamics simulations.  Back
 
Keywords:
AI Application Deployment and Inference, Computational Biology and Chemistry, GTC Silicon Valley 2018 - ID S8827
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Designing Large-Scale Machine Learning Systems with NVIDIA GPUs and Mellanox Interconnect
Gil Bloch (Mellanox Technologies)
Come join us and learn how to build a data-centric GPU cluster for artificial intelligence. Mellanox is a leader in high-performance, scalable, low-latency network interconnects for both InfiniBand and Ethernet. We'll present the state of the art te ...Read More
Come join us and learn how to build a data-centric GPU cluster for artificial intelligence. Mellanox is a leader in high-performance, scalable, low-latency network interconnects for both InfiniBand and Ethernet. We'll present the state of the art techniques for distributed machine learning, and discuss what special requirements they impose on the system, followed by an overview of interconnect technologies used to scale and accelerate distributed machine learning including RDMA, NVIDIA's GPUDirect technology, and a special focus on the in-network computing SHARP technology used to accelerate large scale deployments in artificial intelligence and high performance computing.  Back
 
Keywords:
AI Application Deployment and Inference, Advanced AI Learning Techniques (incl. GANs and NTMs), GTC Silicon Valley 2018 - ID S8635
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Accelerate Your Kaldi Speech Pipeline on the GPU
Hugo Braun (NVIDIA)
Voice commands, and advancements in automatic speech recognition algorithms, that help us interact conversationally with devices, appliances and services, are growing within our everyday environment. We will share some highlights and results from wor ...Read More
Voice commands, and advancements in automatic speech recognition algorithms, that help us interact conversationally with devices, appliances and services, are growing within our everyday environment. We will share some highlights and results from work scheduling optimizations in the Kaldi framework. The first part of the talk will describe results focused primarily on optimizing the DNN components of speech pipeline. We will then show results from a GPU optimized fast lattice decode algorithm to achieve high end to end throughput across the whole ASR pipeline from the acoustic model to the language model.  Back
 
Keywords:
AI Application Deployment and Inference, AI and DL Research, GTC Silicon Valley 2018 - ID S81034
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Accelerating Large-Scale Video Surveillance for Smart Cities with TensorRT
Shounan An (SK Telecom)
We'll discuss a detailed scale-up method for accelerating deep learning-based object detection inference engine with INT8 by using NVIDIA's TensorRT. Previously, converting convolutional neural networks (CNNs) from 32-bit floating-point arithmetic ...Read More
We'll discuss a detailed scale-up method for accelerating deep learning-based object detection inference engine with INT8 by using NVIDIA's TensorRT. Previously, converting convolutional neural networks (CNNs) from 32-bit floating-point arithmetic (FP32) to 8-bit integer (INT8) for classification tasks has been researched. However, there is no solid work for accelerating CNN-based object detection tasks. We'll explain how to accelerate YOLO-v2, the state-of-the-art CNN-based object detector with TensorRT using INT8. We improved YOLO-v2 network for better acceleration and more accurate for surveillance and named our network SIDNet. We verified SIDNet on several benchmark object detection and intrusion detection datasets and confirmed that SIDNet with INT8 has only 1% accuracy drop compared with FP32 mode and is 5x faster than the original YOLO-v2 on NVIDIA Tesla P40.  Back
 
Keywords:
AI Application Deployment and Inference, Telecom Industry Solutions, Deep Learning and AI Frameworks, Computer Vision, Robotics & Autonomous Machines, GTC Silicon Valley 2018 - ID S8296
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Applying AI to Simplify Support- Lessons Learnt
Satish Mandalika (Drishyam.ai)
We'll provide insights into how customer support built on the foundation of AI can help streamline customer support for large enterprises, especially manufacturers. With AI technologies like image recognition and natural language processing maturing ...Read More
We'll provide insights into how customer support built on the foundation of AI can help streamline customer support for large enterprises, especially manufacturers. With AI technologies like image recognition and natural language processing maturing, enterprises should strongly consider building an AI-based support platform, especially those with an omni-channel strategy. Delivering an amazing and differentiated user experience will lead to higher net promoter and customer satisfaction scores. By employing AI-based technologies, enterprises can reduce their contacts, consequently reducing their cost and contact. It will also help them sell more replacement parts online.  Back
 
Keywords:
AI Application Deployment and Inference, NVIDIA Inception Program, Video and Image Processing, GTC Silicon Valley 2018 - ID S8517
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Simulate and Validate your DNN Inference with CATIA before ADAS Industrial Deployment
Simon Berard (Dassault Systèmes), Cecile Doan (Dassault Systèmes)
One of the tough aspect of Deep Neural Network resides in its behavior validation. Although actual driving should be achieved with physical cars to train the neural network, there is today no tool to appropriately prepare data acquisition campaign or ...Read More
One of the tough aspect of Deep Neural Network resides in its behavior validation. Although actual driving should be achieved with physical cars to train the neural network, there is today no tool to appropriately prepare data acquisition campaign or go through stress validation before further on-road testing and industrial deployment. This talk will show how hardware and software in the loop on 3DEXPERIENCE CATIA, can now be extended to AI in the loop, with the ability to activate the full system engineering simulation with the actual neural network meant to run in the autonomous vehicle, accurately reproducing the neural network inference and checking overall vehicle behavior in various conditions. Every stage from full 3D synthetic data ingest and real-time software simulation, through actual hardware in the loop validation both use cases leveraging TensorRT GPU inference can now consistently be proofed for appropriate in-depth understanding of the network reactions before it drives on the road. A POC showing TensorRT and DNN behavior validation will be presented in details, opening new opportunities to validate GPU inference but also compare actual performance impact versus CPU  Back
 
Keywords:
AI Application Deployment and Inference, Product & Building Design, GTC Silicon Valley 2018 - ID S8748
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Deep Learning for Industrial Inspection Analysis
Paul Baines (Wise.io from GE Digital)
We'll show how GE combines extensive domain knowledge with modern deep learning techniques to build intelligent pipeline inspection systems. GE builds a variety of industrial inspection equipment from ultrasonic pipeline inspection gauges to large-s ...Read More
We'll show how GE combines extensive domain knowledge with modern deep learning techniques to build intelligent pipeline inspection systems. GE builds a variety of industrial inspection equipment from ultrasonic pipeline inspection gauges to large-scale CT scanners. As historical producers of hardware, GE is now leading the transformation of the industrial space by building intelligent ecosystems around industrial equipment and processes. Challenges in this space include the esoteric domain-specific nature of the data, as well as the risk averse nature of the industry. However, by leveraging deep learning on large amounts of inspection data, we have been able to build a production system that enhances the reliability and consistency of the inspection process.  Back
 
Keywords:
AI Application Deployment and Inference, Industrial Inspection, GTC Silicon Valley 2018 - ID S8657
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GBM Inferencing on GPU
Vinay Deshpande (NVIDIA), Shankara Rao Thejasw Nanditale (NVIDIA)
We'll present a novel GPU implementation for batched GBM inferencing. We'll also present detailed performance comparison of our implementation against the state-of-the-art libraries such as XGBoost and Treelite. We'll then compare inference perfor ...Read More
We'll present a novel GPU implementation for batched GBM inferencing. We'll also present detailed performance comparison of our implementation against the state-of-the-art libraries such as XGBoost and Treelite. We'll then compare inference performance on various real-world datasets.  Back
 
Keywords:
AI Application Deployment and Inference, Accelerated Analytics, AI and DL Research, GTC Silicon Valley 2018 - ID S8873
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How Microservices and Serverless Computing Enable the Next Generation of Machine Intelligence
Diego Oppenheimer (Algorithmia)
We'll discuss why AI and machine learning are a natural fit for serverless computing and a general architecture for scalable and serverless machine learning in production. We'll discuss issues encountered during implementing our own on-demand scali ...Read More
We'll discuss why AI and machine learning are a natural fit for serverless computing and a general architecture for scalable and serverless machine learning in production. We'll discuss issues encountered during implementing our own on-demand scaling over GPU clusters, show how these apply to more general solutions, and present one possible vision for the future of cloud-based machine learning.  Back
 
Keywords:
AI Application Deployment and Inference, NVIDIA Inception Program, Accelerated Analytics, GTC Silicon Valley 2018 - ID S8900
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AI Startup
Presentation
Media
Deep Learning: An Artificial Brain That Detects Any Type of Cyber Threat
Eli David (Deep Instinct)
Join our presentation on the first application of deep learning to cybersecurity. Deep learning is inspired by the brain's ability to learn: once a brain learns to identify an object, its identification becomes second nature. Similarly, as a ...Read More

Join our presentation on the first application of deep learning to cybersecurity. Deep learning is inspired by the brain's ability to learn: once a brain learns to identify an object, its identification becomes second nature. Similarly, as a deep learning-based artificial brain learns to detect any type of cyber threat, its prediction capabilities become instinctive. As a result, the most evasive and unknown cyber-attacks are immediately detected and prevented. We'll cover the evolution of artificial intelligence, from old rule-based systems to conventional machine learning models until current state-of-the-art deep learning models. 

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Keywords:
AI Startup, Deep Learning and AI, GTC Silicon Valley 2017 - ID S7844
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Disrupting Cancer Diagnostics - Cloud-based Deep Learning AI for Gigantic Pathology Images
Kaisa Helminen (Fimmic)
We'll introduce a novel approach to digital pathology analytics, which brings together a powerful image server and deep learning based image analysis on a cloud platform. Recent advances in AI and Deep Learning in particular show great promi ...Read More

We'll introduce a novel approach to digital pathology analytics, which brings together a powerful image server and deep learning based image analysis on a cloud platform. Recent advances in AI and Deep Learning in particular show great promise in several fields of medicine, including pathology. Human expert judgement augmented by deep learning algorithms has the potential to speed up the diagnostic process and to make diagnostic assessments more reproducible. One of the major advantages of the novel AI-based algorithms is the ability to train classifiers for morphologies that exhibit a high level of complexity. We will present examples on context-intelligent image analysis applications, including e.g. fully automated epithelial cell proliferation assay and tumor grading. We will also present other examples of complex image analysis algorithms, which all run on-demand on whole-slide images in the cloud computing environment. Our WebMicroscope® Cloud is sold as a service (SaaS) approach, which is extremely easy to set up from a user perspective, as the need for local software and hardware installation is removed and the solution can immediately be scaled to projects of any size.

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Keywords:
AI Startup, Healthcare and Life Sciences, Medical Imaging and Radiology, GTC Silicon Valley 2017 - ID S7856
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Intelligent Automation using Deep Learning in Financial Services - Banking to Insurance
Vinay KumarSankarapu (Arya.ai)
Long term goal of any financial institution is achieve the ability to address users with utmost experience within the boundaries of resources. It could only be a possibility when financial institutions adapt to intelligent systems. The success o ...Read More

Long term goal of any financial institution is achieve the ability to address users with utmost experience within the boundaries of resources. It could only be a possibility when financial institutions adapt to intelligent systems. The success of such systems depends heavily on the intelligence. Deep Learning has provided a huge opportunity for financial institutions to start building and planning for such large scale intelligent systems which are multi-functional and adapt. In this talk, we will discuss about how we used Deep Learning, Vega as the platform and GPUs to build high scale automation use cases in Fraud detection to complex process automation in both banking and insurance.

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Keywords:
AI Startup, Deep Learning and AI, Finance, GTC Silicon Valley 2017 - ID S7864
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AI and DL Business Track (high level)
Presentation
Media
Moving Deep Learning from Concept to Production
Jeff Karmiol (IBM)
Spectrum Conductor with Deep Learning capabilities is an end-to-end analytics software engine for the Data Scientist, and is optimized for accelerated hardware. It's designed to support a multi-tenant, on-premises deployment for Deep Learning with a ...Read More
Spectrum Conductor with Deep Learning capabilities is an end-to-end analytics software engine for the Data Scientist, and is optimized for accelerated hardware. It's designed to support a multi-tenant, on-premises deployment for Deep Learning with and end-to-end solution means customers gain business value within each phase of the deep learning pipeline. In this session, we will explore the phases of the pipeline (Setup/Configuration, Data Preparation & Ingestion, Model Training, Deploy & Inference, and Model Maintenance) and provide insights into the unique IBM value for accelerating the use of Deep Learning across a wide variety of industries.  Back
 
Keywords:
AI and DL Business Track (high level), GTC Washington D.C. 2017 - ID DC7265
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AI for Social Good as an Innovation Driver
Ben Hamner (Kaggle), Catherine Ordun (Booz Allen Hamilton), Josh Sullivan (Booz Allen Hamilton), Richard Wender (American Cancer Society)
Innovation can take many forms, and led by varying stakeholders across an organization. One successful model is utilizing AI for Social Good to drive a proof-of-concept that will advance a critical strategic goal. The Data Science Bowl (DSB) is ...Read More

Innovation can take many forms, and led by varying stakeholders across an organization. One successful model is utilizing AI for Social Good to drive a proof-of-concept that will advance a critical strategic goal. The Data Science Bowl (DSB) is an ideal example, launched by Booz Allen Hamilton in 2014, it galvanizes thousands of data scientists to participate in competitions that will have have far reaching impact across key industries such as healthcare. This session will explore the DSB model, as well as look at other ways organizations are utilizing AI for Social Good to create business and industry transformation.

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Keywords:
AI and DL Business Track (high level), AI for Business, GTC Silicon Valley 2018 - ID S8953
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Success in the Age of AI
Michael Sutcliff (Accenture)
From healthcare to financial services to retail, businesses are seeing unprecedented levels of efficiencies and productivity, which will only continue to rise and transform how companies operate. This session will look at how Accenture as an ent ...Read More

From healthcare to financial services to retail, businesses are seeing unprecedented levels of efficiencies and productivity, which will only continue to rise and transform how companies operate. This session will look at how Accenture as an enterprise is optimizing itself in the age of AI, as well as how it guides its customers to success. A look at best practices, insights, and measurement to help the audience inform their AI roadmap and journey.

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Keywords:
AI and DL Business Track (high level), AI for Business, GTC Silicon Valley 2018 - ID S8984
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From Dark Matter Detection to Deep Learning in Enterprise
Scott Stephenson (Deepgram)
Advancements in deep learning are enabling enterprise companies to make meaningful impacts to bottom-line profits. Enterprises capture thousands of hours of customer phone call recordings per day. This voice data is extremely valuable because it cont ...Read More
Advancements in deep learning are enabling enterprise companies to make meaningful impacts to bottom-line profits. Enterprises capture thousands of hours of customer phone call recordings per day. This voice data is extremely valuable because it contains insights that the business can use to improve customer experience and operations. We'll follow Deepgram CEO Dr. Scott Stephenson's path from working in a particle physics lab two miles underground to founding a deep learning company for voice understanding. We'll describe applications of cutting-edge AI techniques to make enterprise voice datasets mineable for valuable business insights. Companies today use these insights to drive the bottom line.  Back
 
Keywords:
AI and DL Business Track (high level), Telecom Industry Solutions, Speech and Language Processing, NVIDIA Inception Program, GTC Silicon Valley 2018 - ID S8274
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The Face Will State The Case
Lisa Hammitt (Beseeq), Rebecca Krauthamer (Beseeq)
We have all heard about Facial Expression and Recognition Systems (FERS) and emotion capture but curiosity looms large. Is it training sets born of Generative Adversarial Networks (GANs) along with GPU architectures that will catapult this technolog ...Read More
We have all heard about Facial Expression and Recognition Systems (FERS) and emotion capture but curiosity looms large. Is it training sets born of Generative Adversarial Networks (GANs) along with GPU architectures that will catapult this technology forward? To be sure, but, something much deeper - a revolution within Computer Science programs in the schools - will accelerate its arrival in consumer platforms. It's called Social Signal Processing and women technologists have a competitive advantage in inventing and enhancing the deep learning algorithms that will fuel it. Come and listen to an industry veteran with 28 years in Artificial Intelligence, including her driving Watson into consumer platforms and a graduate of Stanford University, bolstered by her solid research in Symbolic Systems, discuss their patent-pending technology in the exciting area of Social Signal Processing and FERS. They are both frequent speakers on the ethics of AI usage and will offer their thoughts about how this new class of technology offers a new deal for women to shape the future of AI.  Back
 
Keywords:
AI and DL Business Track (high level), AI and DL Research, GTC Silicon Valley 2018 - ID S8939
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Matching DS Organizational Maturity to DS Skills to Optimally Grow Your Team
Jesse Spencer-Smith (HCA Healthcare)
An organization''s data science needs change dramatically as they move through stages of data science maturity--their ability to consume, adopt, and deploy advanced analytics solutions. Understanding the maturity stage of your organization will help ...Read More
An organization''s data science needs change dramatically as they move through stages of data science maturity--their ability to consume, adopt, and deploy advanced analytics solutions. Understanding the maturity stage of your organization will help you choose projects that can bring value, grow your ability to derive greater value in the future, and help you make good decisions when growing your data science team. A data scientist might be a journeyman model builder, or a data scientist consultant, or a software engineer, or a developer of new deep learning algorithms. The data scientist that would be successful in a mature organization may well fail in an organization new to data science. Hiring and growing data scientists based on skill sets in line with your data science maturity stage and maximizes your probability of success. We''ll discuss a framework to determine your level of data science readiness, explore a tool to assess the skill sets of data scientists, and find which skills can maximize your organization''s probability of success at each stage.  Back
 
Keywords:
AI and DL Business Track (high level), GTC Silicon Valley 2018 - ID S8954
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Rapid Pace of Change and Industry Progress
John Abbott (451 Research), Nick Patience (451 Research)
We are still in the early stages of AI, and its impact on industries is already significant - from healthcare to financial services to retail. Businesses are seeing unprecedented levels of efficiencies and productivity, which will only continue to ri ...Read More
We are still in the early stages of AI, and its impact on industries is already significant - from healthcare to financial services to retail. Businesses are seeing unprecedented levels of efficiencies and productivity, which will only continue to rise and transform how companies operate. This session will explore the progress of AI adoption over the last year, the industries that are leaping ahead, new AI innovations that will serve cross-industry concerns, and what businesses should expect in terms of adoption maturity in 2018.  Back
 
Keywords:
AI and DL Business Track (high level), GTC Silicon Valley 2018 - ID S8952
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Scaling AI POCs Across the Enterprise
Omar Dhalla (Element AI)
Has your team developed an AI proof-of-concept with promising metrics? Next step is to broaden the scope to impact larger areas of the enterprise. With its unique challenges and complexities, scaling POCs across multiple business units is a significa ...Read More
Has your team developed an AI proof-of-concept with promising metrics? Next step is to broaden the scope to impact larger areas of the enterprise. With its unique challenges and complexities, scaling POCs across multiple business units is a significant part of any company''s AI roadmap. This session will look at best practices, insights and success, rooted in Element AI''s experience with enterprise customers.  Back
 
Keywords:
AI and DL Business Track (high level), NVIDIA Inception Program, GTC Silicon Valley 2018 - ID S8989
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Real-Time Genetic Analysis Enabled by GPU
Wayne Thompson (SAS)
For enterprises daunted by the prospect of AI and investing in a new technology platform, the reality is that AI can leverage already-in-place big data and cloud strategies. This session will explore AI and deep learning use cases that are desig ...Read More

For enterprises daunted by the prospect of AI and investing in a new technology platform, the reality is that AI can leverage already-in-place big data and cloud strategies. This session will explore AI and deep learning use cases that are designed for ROI, and look at how success is being measured and optimized.

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Keywords:
AI and DL Business Track (high level), AI for Business, GTC Silicon Valley 2018 - ID S8983
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The Extreme Data Economy: How Businesses Thrive in the Post Big Data Era (Presented by Kinetica)
Daniel Raskin (Kinetica)
Get the latest information on how the proliferation of mobile, cloud, and IoT devices has brought us into a new era: The Extreme Data Economy. There''s a greater variety of data than ever before, and exponentially more of it, streaming in real time. ...Read More
Get the latest information on how the proliferation of mobile, cloud, and IoT devices has brought us into a new era: The Extreme Data Economy. There''s a greater variety of data than ever before, and exponentially more of it, streaming in real time. Across industries, companies are turning data into an asset, above and beyond any product or service they offer. But unprecedented agility is required to keep business in motion and succeed in this post-big data era. To enable this level of agility, companies are turning to instant insight engines that are powered by thousands of advanced GPU cores, bringing unparalleled speed, streaming data analysis, visual foresight, and machine learning to break through the old bottlenecks. Learn about new data-powered use cases you''ll need to address, as well as advances in computing technology, particularly accelerated parallel computing, that will translate data into instant insight to power business in motion.  Back
 
Keywords:
AI and DL Business Track (high level), NVIDIA Inception Program, GTC Silicon Valley 2018 - ID S8997
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Create Customer Value with Google Cloud AI (Presented by Google)
Chris Kleban (Google Inc.)
In this session, you will learn how Google Cloud helps enterprises make the most out of data, and deliver customer value. We will provide an in-depth overview of the Cloud AI and Data Analytics offering that helps enterprises manage their ML lifecycl ...Read More
In this session, you will learn how Google Cloud helps enterprises make the most out of data, and deliver customer value. We will provide an in-depth overview of the Cloud AI and Data Analytics offering that helps enterprises manage their ML lifecycle, from data ingestion to insights and prediction. We will also demonstrate some breakthrough solutions, like AutoML, that are making ML accessible to everyone.  Back
 
Keywords:
AI and DL Business Track (high level), GTC Silicon Valley 2018 - ID S8976
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Trends and Opportunities for ML and AI in Consumer Insights Industries
Paul Hendricks (NVIDIA), Eric Thorsen (NVIDIA)
We'll examine business value drivers for artificial intelligence and machine learning in retail and consumer goods industries. Traditionally, traction in AI and ML has been in deep research, scientific, and technical communities. Retailers and consu ...Read More
We'll examine business value drivers for artificial intelligence and machine learning in retail and consumer goods industries. Traditionally, traction in AI and ML has been in deep research, scientific, and technical communities. Retailers and consumer products companies are finding great success applying AI and ML technology to distinct use cases and business challenges. Join us to hear project descriptions and customer examples where AI and ML can impact the business by increasing revenue, protecting margin, and improving consumer satisfaction.  Back
 
Keywords:
AI and DL Business Track (high level), Virtual Reality and Augmented Reality, Consumer Engagement and Personalization, GTC Silicon Valley 2018 - ID S8131
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Practical Use Cases Of AI and Deep Learning On GPUs In The Cloud For Marketing And Retail
Alexander Tsyplikhin (Data Monsters)
We'll review three practical use cases of applying AI and deep learning in the marketing and retail industries. For each use case, we'll cover business situations, discuss potential approaches, and describe final solutions from both the AI and infr ...Read More
We'll review three practical use cases of applying AI and deep learning in the marketing and retail industries. For each use case, we'll cover business situations, discuss potential approaches, and describe final solutions from both the AI and infrastructural points of view. Attendees will learn about applications of AI and deep learning in marketing and advertising; AI readiness criteria; selecting the right AI and deep learning methods, infrastructure, and GPUs for specific use cases; and avoiding potential risks.  Back
 
Keywords:
AI and DL Business Track (high level), Predictive Analytics for Retail, Consumer Engagement and Personalization, GTC Silicon Valley 2018 - ID S8265
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Earth Observation From Space: Deep Learning based Satellite Image Analysis
Patrick Helber (German Research Center for Artificial Intelligence)
Learn how recent advances in Earth observation are opening up a new exciting area for exploration of satellite image data with deep learning. Focusing on real-world scenarios, we will teach you how to analyze this exciting remote sensing data source ...Read More
Learn how recent advances in Earth observation are opening up a new exciting area for exploration of satellite image data with deep learning. Focusing on real-world scenarios, we will teach you how to analyze this exciting remote sensing data source with deep neural networks. An automated satellite image understanding is of high interest for various research fields and industry sectors such as the insurance, agriculture or investing industry. You will learn how to apply deep neural networks in natural disaster situations and for the classification of land-use, land-cover and building types.  Back
 
Keywords:
AI and DL Business Track (high level), GIS, AI and DL Research, GTC Silicon Valley 2018 - ID S81028
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AI and DL Research
Presentation
Media
Training Neural Networks with Mixed Precision: Real Examples
Benjamin Barsdell (NVIDIA), Michael O'Connor (NVIDIA), Christian M. Sarofeen (NVIDIA)
We will cover the techniques for training DNNs with Tensor Cores described in "S8923 - Training Neural Networks with Mixed Precision: Theory and Practice". These methods were introduced for AI processing with the Volta GPU architecture. T ...Read More
We will cover the techniques for training DNNs with Tensor Cores described in "S8923 - Training Neural Networks with Mixed Precision: Theory and Practice". These methods were introduced for AI processing with the Volta GPU architecture. Tensor Cores provide up to 120 TFlops throughput, mixing operations on IEEE half- and single-precision floats. Techniques used will include loss-scaling, master weights copy, and choosing the proper precision for a given operation. For each of TensorFlow and PyTorch we will describe a fp32 network definition and then demonstrate the same network using mixed precision techniques.  Back
 
Keywords:
AI and DL Research, Algorithms and Numerical Techniques, GTC Silicon Valley 2018 - ID S81012
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Supporting a DGX Air-Gapped Production Environments
Sumit Kumar (NVIDIA), Jeffrey Weiss (NVIDIA)
This tutorial will cover the issues encountered when deploying NVIDIA DGX-1/DGXStation into secure environment. For security reasons, some installations require that systems be isolated from the internet or outside networks. Since most DGX-1 softwar ...Read More
This tutorial will cover the issues encountered when deploying NVIDIA DGX-1/DGXStation into secure environment. For security reasons, some installations require that systems be isolated from the internet or outside networks. Since most DGX-1 software updates are accomplished through an over-the-network process with NVIDIA servers, this session will walk the participants through how updates can be made by maintaining an intermediary server. This session will be a combination of lecture, live demos and along with detailed instructions.  Back
 
Keywords:
AI and DL Research, Data Center and Cloud Infrastructure, GTC Silicon Valley 2018 - ID S8568
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Scaling Machine Learning through Decentralization, Quantization, and Structured Sparsity
Dan Alistarh (IST Austria), Ce Zhang (ETH Zurich)
In this session, participants will get a taste of state-of-the-art techniques for scaling Deep Learning on GPU clusters. We present SuperML, a general and efficient communication layer for machine learning, which can scale neural network training to ...Read More
In this session, participants will get a taste of state-of-the-art techniques for scaling Deep Learning on GPU clusters. We present SuperML, a general and efficient communication layer for machine learning, which can scale neural network training to hundreds of GPU nodes. SuperML builds on three main ideas: decentralization, which allows algorithms to converge without a centralized coordinator (parameter server) or all-to-all communication, communication quantization, which significantly speeds up point-to-point messaging, and structured sparsity, by which SuperML induces model updates which only have a limited number of non-zero entries. From the technical perspective, SuperML provides a new implementation of the classic MPI standard, re-designed and re-implemented to provide efficient support for quantization and sparsity. We illustrate the performance characteristics of SuperML on CSCS Piz Daint, Europe's most powerful supercomputer, and on Amazon EC2, improving upon other highly optimized implementations such as CrayMPI and NVIDIA NCCL.  Back
 
Keywords:
AI and DL Research, Accelerated Analytics, HPC and Supercomputing, GTC Silicon Valley 2018 - ID S8668
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Designing Wireless Systems with Deep Learning - An Autoencoder-Based Approach to PHY Layer Design
Ben Hilburn (DeepSig Inc.), Tim O'Shea (DeepSig Inc.)
The field of wireless engineering is on the cusp of a revolution, driven by deep learning, that will define the next paradigm in wireless system design. While wireless communications technology has advanced considerably since its invention in the 189 ...Read More
The field of wireless engineering is on the cusp of a revolution, driven by deep learning, that will define the next paradigm in wireless system design. While wireless communications technology has advanced considerably since its invention in the 1890s, the fundamental design methodology has remained unchanged throughout its history - expert engineers hand-designing radio systems for specific applications. Deep learning enables a new, radically different approach, where systems are learned from wireless channel data. As the world becomes more connected and the Internet of Things becomes a reality, it is difficult to overstate the enormity of the impact to both commercial and military systems. This talk will provide a high-level overview of deep learning applied to wireless communications, discuss the current state of the technology and research, and present a vision for the future of wireless engineering.  Back
 
Keywords:
AI and DL Research, Telecom Industry Solutions, GTC Silicon Valley 2018 - ID S8791
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Domain Adaptation Using Adversarial Training for Semantic Segmentation and Caption Style Transfer
Min Sun (National Tsing Hua University)
We'll introduce the basic concept of domain adaptation and how to use adversarial training to achieve unsupervised domain adaptation. We'll then describe how the technique is used in two tasks: improving semantic segmentation across cities, and tr ...Read More
We'll introduce the basic concept of domain adaptation and how to use adversarial training to achieve unsupervised domain adaptation. We'll then describe how the technique is used in two tasks: improving semantic segmentation across cities, and transferring language style for image captioning. In particular, we combine domain adaptation with policy gradient-based reinforcement learning approach to transfer language style. The details and results of both tasks are published in ICCV 2017.  Back
 
Keywords:
AI and DL Research, GTC Silicon Valley 2018 - ID S8200
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Deep Learning Applications for Radio Frequency (RF) Data
Adam Thompson (NVIDIA)
We'll discuss applications of deep learning to radio frequency (RF) data including specific signal and digital modulation scheme classification, identification of nefarious activities, and a general overview of the unique challenges and solutions fo ...Read More
We'll discuss applications of deep learning to radio frequency (RF) data including specific signal and digital modulation scheme classification, identification of nefarious activities, and a general overview of the unique challenges and solutions for AI in this domain. With the ubiquity of RF communication signals in our lives, deep learning can be leveraged to ensure accurate signal transmission and safer communities.  Back
 
Keywords:
AI and DL Research, Computational Physics, GTC Silicon Valley 2018 - ID S8826
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Simultaneous Pixel-Localization and Feature Extraction for Multiple Instances in a Scene
Timothy Klein (Arete Associates)
We'll introduce attendees to a new deep learning approach to object-localization. Instead of bounding boxes, our network estimates the center pixel locations for a variable number of targets in a scene while simultaneously extracting a characteristi ...Read More
We'll introduce attendees to a new deep learning approach to object-localization. Instead of bounding boxes, our network estimates the center pixel locations for a variable number of targets in a scene while simultaneously extracting a characteristic feature-set. We'll outline the overall approach and describe the underlying network architecture and training. We'll also present the results of our network as applied to the cars overhead with context dataset and discuss the current and future possibilities of this approach.  Back
 
Keywords:
AI and DL Research, Computer Vision, GTC Silicon Valley 2018 - ID S8191
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Inside NVIDIA GPU Cloud Deep Learning Framework Containers
John Barco (NVIDIA), Christopher Lamb (NVIDIA)
In this technical deep dive, get an in-depth look at the deep learning containers on NVIDIA GPU Cloud (NGC) and learn how they can simplify your AI projects. NVIDIA pre-integrates and optimizes the top deep learning frameworks such as TensorFlow, PyT ...Read More
In this technical deep dive, get an in-depth look at the deep learning containers on NVIDIA GPU Cloud (NGC) and learn how they can simplify your AI projects. NVIDIA pre-integrates and optimizes the top deep learning frameworks such as TensorFlow, PyTorch, and MXNet, and makes them available on NVIDIA GPU Cloud, removing time consuming do-it-yourself software integration. We'll look at the NVIDIA framework optimizations, such as reducing GPU memory overhead, improving multi-GPU scaling, and reducing latency. And we'll talk about the integration of runtimes and drivers in the containers to ensure the correct versions of software are working together for peak performance. You'll leave with an understanding of what make an NVIDIA GPU-optimized deep learning container tick.  Back
 
Keywords:
AI and DL Research, Deep Learning and AI Frameworks, Data Center and Cloud Infrastructure, GTC Silicon Valley 2018 - ID S8497
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Matchbox: Automatic Batching for Dynamic Deep Learning
James Bradbury (Salesforce)
Matchbox is an open source PyTorch-based tool that lets users implement their deep learning models as imperative code that applies to individual data samples, then efficiently train and validate them on batched data using GPUs. By automatically keepi ...Read More
Matchbox is an open source PyTorch-based tool that lets users implement their deep learning models as imperative code that applies to individual data samples, then efficiently train and validate them on batched data using GPUs. By automatically keeping track of batch-level masking and padding and rewriting data-dependent control flow, Matchbox simplifies model code, eliminates a class of implementation bugs, and allows programmers to work directly at a more natural level of abstraction.  Back
 
Keywords:
AI and DL Research, Deep Learning and AI Frameworks, GTC Silicon Valley 2018 - ID S8977
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Tackling the Crowded Radio Frequency Spectrum Using Deep Learning
Krishna Karra (KickView Corporation)
We'll introduce new concepts and algorithms that apply deep learning to radio frequency (RF) data to advance the state of the art in signal processing and digital communications. With the ubiquity of wireless devices, the crowded RF spectrum ...Read More

We'll introduce new concepts and algorithms that apply deep learning to radio frequency (RF) data to advance the state of the art in signal processing and digital communications. With the ubiquity of wireless devices, the crowded RF spectrum poses challenges for cognitive radio and spectral monitoring applications. Furthermore, the RF modality presents unique processing challenges due to the complex-valued data representation, large data rates, and unique temporal structure. We'll present innovative deep learning architectures to address these challenges, which are informed by the latest academic research and our extensive experience building RF processing solutions. We'll also outline various strategies for pre-processing RF data to create feature-rich representations that can significantly improve performance of deep learning approaches in this domain. We'll discuss various use-cases for RF processing engines powered by deep learning that have direct applications to telecommunications, spectral monitoring, and the Internet of Things.

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Keywords:
AI and DL Research, Telecom Industry Solutions, Federal, GTC Silicon Valley 2018 - ID S8267
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Point Cloud Deep Learning
Innfarn Yoo (NVIDIA)
This presentation shows in-depth comparisons of several neural network models for 3D object classification. Object classification from 2D image is studied thoroughly and widely adopted during last few years by following the advances of deep neural ne ...Read More
This presentation shows in-depth comparisons of several neural network models for 3D object classification. Object classification from 2D image is studied thoroughly and widely adopted during last few years by following the advances of deep neural networks. From then, 3D object classification methods are actively studied, and yet not completely mature. Point cloud is most basic format of 3D objects. In this work, we present many neural network models that can be learned from 3D point cloud. It includes directly learning from 3D point cloud, projected 2D pixels, and voxelated volumes. This work uses Princeton ModelNet datasets and ShapeNetCore.v2 dataset, and then provides the comparisons of those neural network models.  Back
 
Keywords:
AI and DL Research, Graphics and AI, Rendering and Ray Tracing, Real-Time Graphics, GTC Silicon Valley 2018 - ID S8453
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GUNREAL: GPU-Accelerated Unsupervised Reinforcement and Auxiliary Learning
Koichi Shirahata (Fujitsu Laboratories Ltd.)
We'll introduce GPU-accelerated unsupervised reinforcement and auxiliary learning (UNREAL) algorithm. Recent state-of-the-art deep reinforcement learning algorithms, such as A3C and UNREAL, are designed to train on a single device with only CPUs. Us ...Read More
We'll introduce GPU-accelerated unsupervised reinforcement and auxiliary learning (UNREAL) algorithm. Recent state-of-the-art deep reinforcement learning algorithms, such as A3C and UNREAL, are designed to train on a single device with only CPUs. Using GPU acceleration for these algorithms results in low GPU utilization, which means the full performance of the GPU is not reached. Motivated by the architecture changes made by the GA3C algorithm, which gave A3C better GPU acceleration, together with the high learning efficiency of the UNREAL algorithm, we extend GA3C with the auxiliary tasks from UNREAL to create GUNREAL. We show that our GUNREAL system finished training faster than UNREAL and reached higher scores than GA3C.  Back
 
Keywords:
AI and DL Research, Performance Optimization, GTC Silicon Valley 2018 - ID S8219
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Large-Scale Self-Supervised Robot Learning with GPU-Enabled Video-Prediction Models
Frederik Ebert (UC Berkeley)
To acquire rich repertoires of skills, robots must be able to learn from their own autonomously collected data. We'll describe a video-prediction model that predicts what a robot will see next, and show how this model can be used to solve complex ma ...Read More
To acquire rich repertoires of skills, robots must be able to learn from their own autonomously collected data. We'll describe a video-prediction model that predicts what a robot will see next, and show how this model can be used to solve complex manipulations tasks in real-world settings. Our model was trained on 44,000 video sequences, where the manipulator autonomously pushes various objects. Using the model, the robot is capable of moving objects that were not seen during training to desired locations, handling multiple objects and pushing objects around obstructions. Unlike other methods in robotic learning, video-prediction does not require any human labels. Our experiments show that the method achieves a significant advance in the range and complexity of skills that can be performed entirely with self-supervised robotic learning. This session is for attendees that possess a basic understanding of convolutional and recurrent neural networks.  Back
 
Keywords:
AI and DL Research, IoT, Robotics & Drones, Robotics & Autonomous Machines, GTC Silicon Valley 2018 - ID S8629
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Deep Generative Modeling for Speech Synthesis and Sensor Data Augmentation
Praveen Narayanan (Ford Motor Company)
We'll discuss how we could use deep generative modeling in two application domains; in speech synthesis, and in sensor data modeling. We'll give an overview of what generative modeling is and how it could be used for practical AI tasks through the ...Read More
We'll discuss how we could use deep generative modeling in two application domains; in speech synthesis, and in sensor data modeling. We'll give an overview of what generative modeling is and how it could be used for practical AI tasks through these examples. We'll also give a flavor of latent space methods, which we can use to learn more about our data so as to transform them in meaningful ways, with uses in both reconstruction and in generation.  Back
 
Keywords:
AI and DL Research, Advanced AI Learning Techniques (incl. GANs and NTMs), GTC Silicon Valley 2018 - ID S8617
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New Applications of Deep Learning in Dialogue Generation and Question Answering
Mithun Das Gupta (Microsoft)
The current generation AI systems are mostly moving towards dialogue generation and question answering. Human like conversation and dialogue based interaction has been proposed as the interface for tomorrow, which would obliterate key-boards and trac ...Read More
The current generation AI systems are mostly moving towards dialogue generation and question answering. Human like conversation and dialogue based interaction has been proposed as the interface for tomorrow, which would obliterate key-boards and track-pads from computers as we know them. We present two important current developments in these fields. First we talk about a neural dialogue generation system which can be deployed to engage humans in a multi-turn conversation. Next we talk about a segmented question answering module which can find answers from the web. The combination of these two techniques has the potential to unlock numerous new verticals, such as travel, retail etc. We will talk about the technical details as well as the higher level design choices.  Back
 
Keywords:
AI and DL Research, Speech and Language Processing, Advanced AI Learning Techniques (incl. GANs and NTMs), GTC Silicon Valley 2018 - ID S8151
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Object-Level Deep Reinforcement Learning
William Agnew (University of Washington)
We'll show how deep reinforcement learning can be greatly sped up by separating perception and action, with a reward function specified in terms of objects and their motions, which are supplied by the perceptual system. In the past five years, reinf ...Read More
We'll show how deep reinforcement learning can be greatly sped up by separating perception and action, with a reward function specified in terms of objects and their motions, which are supplied by the perceptual system. In the past five years, reinforcement learners have become vastly more powerful by incorporating deep learning techniques, playing Atari, Mario, Go, and other games with superhuman skill. However, these learners require vast amounts of training data to become skilled. For example, to master Pong, state-of-the-art reinforcement learners require tens of millions of game frames, equivalent to months of play time at human speed. We show that endowing the learner with a minimal perceptual system, capable of detecting and tracking objects, greatly reduces the number of frames needed for learning. This shifts the learning bottleneck from the amount of training data available to computations easily accelerated with GPUs.  Back
 
Keywords:
AI and DL Research, Advanced AI Learning Techniques (incl. GANs and NTMs), GTC Silicon Valley 2018 - ID S8581
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Recent Advances in Neural Machine Translation: Multilingual, Non-Parametric to Unsupervised Neural Machine Translation
Kyunghyun Cho (New York University)
We'll describe the latest advances in neural machine translation from three different perspectives. We'll start with character-level, multilingual neural machine translation, which aims at harnessing positive language transfer among multiple langua ...Read More
We'll describe the latest advances in neural machine translation from three different perspectives. We'll start with character-level, multilingual neural machine translation, which aims at harnessing positive language transfer among multiple languages to improve the translation quality and the robustness of such a multilingual translation model to intra-sentence code-switching and typos. We'll then discuss the recent research on exploiting data beside oft-used parallel corpora. We'll discuss how another modality, such as vision, can be used to enable zero-resource machine translation, and how purely unsupervised neural machine translation can be done by exploiting the similarity between language distributions of two languages. Finally, we'll discuss a recent trend of retrieval-based approaches to deep learning with a specific example of non-parametric neural machine translation.  Back
 
Keywords:
AI and DL Research, Advanced AI Learning Techniques (incl. GANs and NTMs), GTC Silicon Valley 2018 - ID S8609
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Deep Active Learning
Adam Lesnikowski (NVIDIA)
We'll discuss ongoing work at NVIDIA on deep active learning. Attendees can expect to learn what active learning is and some of the challenges of applying it to deep neural network training.
We'll discuss ongoing work at NVIDIA on deep active learning. Attendees can expect to learn what active learning is and some of the challenges of applying it to deep neural network training.  Back
 
Keywords:
AI and DL Research, Advanced AI Learning Techniques (incl. GANs and NTMs), GTC Silicon Valley 2018 - ID S8692
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Unsupervised Image-to-Image Translation Networks
Ming-Yu Liu (NVIDIA)
We'll introduce a GAN-based framework for unsupervised image-to-image translation. It leverages a shared latent space assumption to learn to translate an image in one domain to a corresponding image in another domain without requiring any pair of co ...Read More
We'll introduce a GAN-based framework for unsupervised image-to-image translation. It leverages a shared latent space assumption to learn to translate an image in one domain to a corresponding image in another domain without requiring any pair of corresponding images in the two domains in the training dataset. We'll show examples on translating street scene images, from sunny day to rainy day or from day time to night time. We also show image translation results on dog breed conversions and cat species conversion as well as human face translation based on attributes.  Back
 
Keywords:
AI and DL Research, Computer Vision, GTC Silicon Valley 2018 - ID S8114
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Towards Lifelong Reinforcement Learning
Pulkit Agrawal (UC Berkeley)
Reinforcement learning aims to determine a mapping from observations to actions that maximize a reward criterion. The agent starts off exploring the environment for rewards with random search, which is only likely to succeed in all but simplest of se ...Read More
Reinforcement learning aims to determine a mapping from observations to actions that maximize a reward criterion. The agent starts off exploring the environment for rewards with random search, which is only likely to succeed in all but simplest of settings. Furthermore, measuring and designing reward functions for real-world tasks is non-trivial. Inspired by research in developmental psychology, in this talk I will discuss how reinforcement learning agents might use curiosity and knowledge accumulated from experience for efficient exploration. I will present results illustrating an agent learning to play the game of Mario and learning to navigate without rewards, a study quantifying the kinds of prior knowledge used by humans for efficient exploration and some robotic manipulation experiments including the use of an anthropomorphic hand for grasping objects.   Back
 
Keywords:
AI and DL Research, IoT, Robotics & Drones, Robotics & Autonomous Machines, GTC Silicon Valley 2018 - ID S8217
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How We Can Analyze Profile from Real-Time Conversation by Unsupervised Learning
Shigehisa Omatsu (dAIgnosis,Inc.)
To convert phonemes of telephone conversations and responses at meetings into texts in real time, pass the text to the computational model created by DGX-1, label with a learning without teacher, and add the clusters, we are developing a system which ...Read More
To convert phonemes of telephone conversations and responses at meetings into texts in real time, pass the text to the computational model created by DGX-1, label with a learning without teacher, and add the clusters, we are developing a system which compares objects and analyzes meaning of conversation and profiles of interlocutors. With this technology, customers can receive appropriate responses at the beginning of a conversation with a help desk, and patients can receive correspondence during a remote diagnosis with a doctor based solely off of their dialogue and examination results. By using TensorFlow as a platform and running the K-Means method, Word2vec, Doc2Vec, etc. in DGX-1 clustered environment on DGX-1, the result of arithmetic processing is found at high speed conversation. Even if the amount of sentences is increased, the learning effect increases linearly, demonstrating that the proportion of validity can be raised without taking grammar of languages ??other than English (e.g. Japanese) into account.  Back
 
Keywords:
AI and DL Research, Speech and Language Processing, NVIDIA Inception Program, GTC Silicon Valley 2018 - ID S8371
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Embodied Question Answering
Abhishek Das (Georgia Tech)
Building intelligent agents that possess the ability to perceive the rich visual environment around us, communicate this understanding in natural language to humans and other agents, and execute actions in a physical environment, has been a long-term ...Read More
Building intelligent agents that possess the ability to perceive the rich visual environment around us, communicate this understanding in natural language to humans and other agents, and execute actions in a physical environment, has been a long-term goal of Artificial Intelligence. In this talk, I will present my recent work on an instantiation of this goal -- Embodied Question Answering (EQA) -- where an agent that is spawned at a random location in an environment (a house or building) is asked a natural language question ("What color is the car?"). The agent perceives its environment through first-person vision and can perform a few 'atomic' actions: move-{forward, backward, right, left}, and turn-{right, left}. The objective of the agent is to explore the environment and gather visual information necessary to answer the question ("orange"). I'll introduce our OpenGL-based environments, a large-scale dataset of expert demonstrations for this task and deep models, trained end-to-end using reinforcement learning, from raw pixels to multi-step navigation control to visual question answering.  Back
 
Keywords:
AI and DL Research, Computer Vision, GTC Silicon Valley 2018 - ID S8582
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Meet Horovod: Uber's Open Source Distributed Deep Learning Framework for TensorFlow
Alexander Sergeev (Uber)
Horovod makes it easy to train a single GPU TensorFlow model on many GPUs; both on a single server and across multiple servers. We'll cover Uber's explorations of distributed deep learning, how to use Horovod, and what kind of performance you ...Read More
Horovod makes it easy to train a single GPU TensorFlow model on many GPUs; both on a single server and across multiple servers. We'll cover Uber's explorations of distributed deep learning, how to use Horovod, and what kind of performance you can get on standard models, such as Inception V3 and ResNet-101. Learn how to speed up training of your TensorFlow model with Horovod.  Back
 
Keywords:
AI and DL Research, Deep Learning and AI Frameworks, HPC and AI, GTC Silicon Valley 2018 - ID S8152
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Instance-Aware Image and Sentence Matching with Selective Multimodal LSTM
Yan Huang (Institute of Automation, Chinese Academy of Sciences)
We'll present a unique framework for cross-modal image and sentence matching; namely selective multimodal long short-term memory (LSTM) that incorporates a new deep learning module as multimodal context-modulated attention network to selectively att ...Read More
We'll present a unique framework for cross-modal image and sentence matching; namely selective multimodal long short-term memory (LSTM) that incorporates a new deep learning module as multimodal context-modulated attention network to selectively attend to pairwise semantic concepts. In detail, effective image and sentence matching depends on measuring their global visual-semantic similarity. Based on the observation that such a global similarity arises from a complex aggregation of multiple local similarities between pairwise instances of image (objects) and sentence (words), we propose a selective multimodal LSTM network (sm-LSTM) for instance-aware image and sentence matching. The sm-LSTM includes a multimodal context-modulated attention scheme at each timestep that can selectively attend to a pair of instances of image and sentence by predicting pairwise instance-aware saliency maps for image and sentence. For selected pairwise instances, their representations are obtained based on the predicted saliency maps, and then compared to measure their local similarity. By similarly measuring multiple local similarities within a few timesteps, the sm-LSTM sequentially aggregate.  Back
 
Keywords:
AI and DL Research, Computer Vision, GTC Silicon Valley 2018 - ID S8281
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Towards AI Agents That Can See, Talk, and Act
Dhruv Batra (Georgia Tech and Facebook AI Research)
We are witnessing unprecedented advances in computer vision and AI. What lies next for AI? We believe that the next generation of intelligent systems (say the next generation of Google's Assistant, Facebook's M, Apple's Siri, Amazon's Alexa) will ...Read More
We are witnessing unprecedented advances in computer vision and AI. What lies next for AI? We believe that the next generation of intelligent systems (say the next generation of Google's Assistant, Facebook's M, Apple's Siri, Amazon's Alexa) will need to possess the ability to perceive their environment (through vision, audition, or other sensors), communicate (i.e., hold a natural language dialog with humans and other agents), and act (e.g., aid humans by executing API calls or commands in a virtual or embodied environment), for tasks such as: aiding visually impaired users in understanding their surroundings; interacting with an AI assistant (Human: 'Alexa can you see the baby in the baby monitor?', AI: 'Yes, I can', Human: 'Is he sleeping or playing?'); robotics applications (e.g. search and rescue missions) where the operator may be situationally blind and operating via language. We'll present work from our lab on a range of projects on such visually grounded conversational agents.  Back
 
Keywords:
AI and DL Research, Computer Vision, GTC Silicon Valley 2018 - ID S8571
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Scaling Convolutional Neural Networks with Kubernetes and TensorFlow on AWS GPUs
Reza Zadeh (Matroid)
In this session we present a Kubernetes deployment on Amazon AWS GPUs that provide customized computer vision to a large number of users. Reza offers an overview of Matroid's pipeline and demonstrates how to customize computer vision neural network ...Read More
In this session we present a Kubernetes deployment on Amazon AWS GPUs that provide customized computer vision to a large number of users. Reza offers an overview of Matroid's pipeline and demonstrates how to customize computer vision neural network models in the browser, followed by building, training, and visualizing TensorFlow models, which are provided at scale to monitor video streams.  Back
 
Keywords:
AI and DL Research, Data Center and Cloud Infrastructure, Computer Vision, GTC Silicon Valley 2018 - ID S8610
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Audio Recognition, Context-Awareness, and its Applications
Yoonchang Han (cochlear.ai)
We'll explain the concept and the importance of audio recognition, which aims to understand literally all the information contained in the audio, not limiting its scope to speech recognition. It includes the introduction of various types of non ...Read More
We'll explain the concept and the importance of audio recognition, which aims to understand literally all the information contained in the audio, not limiting its scope to speech recognition. It includes the introduction of various types of non-verbal information contained in the audio such as acoustic scenes/events, speech, and music. This session is helpful to the people who are not familiar with audio processing but are interested in the context-aware system. Also, it might be inspiring for someone who develops AI applications such as AI home assistant, a humanoid robot, and self-driving cars. It also covers the potential use-cases and creative applications, including a video demonstration of the audio context-aware system applied to media-art performance for real-time music generation.  Back
 
Keywords:
AI and DL Research, Speech and Language Processing, NVIDIA Inception Program, GIS, GTC Silicon Valley 2018 - ID S8696
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Trade and Manage Wealth with Deep Reinforcement Learning and Memory
Daniel Egloff (Flink AI)
We'll present how deep reinforcement learning (DRL) and memory extended networks can be used to train agents, which optimize asset allocations or propose trading actions. The memory component is crucial for improved mini-batch parallelization and he ...Read More
We'll present how deep reinforcement learning (DRL) and memory extended networks can be used to train agents, which optimize asset allocations or propose trading actions. The memory component is crucial for improved mini-batch parallelization and helps mitigate catastrophic forgetting. We also address how concepts from risk-sensitive and safe reinforcement learning apply to improve the robustness of the learned policies. The DRL approach has several advantages over the industry standard approach, which is still based on the mean variance portfolio optimization. The most significant benefit is that the information bottleneck between the statistical return model and the portfolio optimizer is removed, and available market data and trade history are used much more efficiently.  Back
 
Keywords:
AI and DL Research, Algorithms and Numerical Techniques, Advanced AI Learning Techniques (incl. GANs and NTMs), Finance, GTC Silicon Valley 2018 - ID S8679
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(Deep) Learning to Grasp with a Close-Loop DNN Controller
Iuri Frosio (NVIDIA), Mengyuan Yan (Stanford University)
The paradigm for robot programming is changing with the adoption of the deep learning approach in the field of robotics. Instead of hard coding a complex sequence of actions, tasks are acquired by the robot through an active learning procedure. This ...Read More
The paradigm for robot programming is changing with the adoption of the deep learning approach in the field of robotics. Instead of hard coding a complex sequence of actions, tasks are acquired by the robot through an active learning procedure. This introduces new challenges that have to be solved to achieve effective training. We'll show several issues that can be encountered while learning a close-loop DNN controller aimed at a fundamental task like grasping, and their practical solutions. First, we'll illustrate the advantages of training using a simulator, as well as the effects of choosing different learning algorithms in the reinforcement learning and imitation learning domains. We'll then show how separating the control and vision modules in the DNN can simplify and speed up the learning procedure in the simulator, although the learned controller hardly generalizes to the real world environment. Finally, we'll demonstrate how to use domain transfer to train a DNN controller in a simulator that can be effectively employed to control a robot in the real world.  Back
 
Keywords:
AI and DL Research, IoT, Robotics & Drones, Computer Vision, Robotics & Autonomous Machines, GTC Silicon Valley 2018 - ID S8132
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Affective Categorization Using Contactless-Based Accelerometers
Refael Shamir (Letos)
We'll cover the four known methods for emotion detection: vision, speech, sentiment analysis, and wearable technology. We'll provide a quick dive through each presented solution, and then introduce a novel approach aimed for the future of autonomou ...Read More
We'll cover the four known methods for emotion detection: vision, speech, sentiment analysis, and wearable technology. We'll provide a quick dive through each presented solution, and then introduce a novel approach aimed for the future of autonomous vehicles.  Back
 
Keywords:
AI and DL Research, Consumer Engagement and Personalization, GTC Silicon Valley 2018 - ID S8352
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Graduate Fellowship FastForward Talks
Robin Betz (Stanford University), Awni Hannun (Stanford University), Robert Konrad (Stanford University), Deepak Pathak (UC Berkeley), Fereshteh Sadeghi (University of Washington), Abigail See (Stanford University), Anna Shcherbina (Stanford University), Caroline Trippel (Princeton University)
Join a special presentation from our 2017-2018 Graduate Fellowship recipients to learn "what's next" out of the world of research and academia. Sponsored projects involve a variety of technical challenges, including distributed systems for ...Read More
Join a special presentation from our 2017-2018 Graduate Fellowship recipients to learn "what's next" out of the world of research and academia. Sponsored projects involve a variety of technical challenges, including distributed systems for large-scale deep learning; dynamic data structures for massively parallel machine learning; machine learning techniques for biomedical image analysis; visual dynamics; and compilation frameworks for high-performance graphics systems. We believe that these minds lead the future in our industry and we're proud to support the 2016-2017 NVIDIA Graduate Fellows. We'll also announce the 2017-2018 Graduate Fellows at this session. For more information on the NVIDIA Graduate Fellowship program, visit www.nvidia.com/fellowship.  Back
 
Keywords:
AI and DL Research, Virtual Reality and Augmented Reality, Graphics and AI, Computational Biology and Chemistry, Computer Vision, GTC Silicon Valley 2018 - ID S8793
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Learning Rigidity in Dynamic Scenes for Scene Flow Estimation
Kihwan Kim (NVIDIA)
Estimation of 3D motion in a dynamic scene from a pair of images is a core task in many scene understanding problems. In real world applications, a dynamic scene is commonly captured by a moving camera (i.e., panning, tilting or hand-held), increasin ...Read More
Estimation of 3D motion in a dynamic scene from a pair of images is a core task in many scene understanding problems. In real world applications, a dynamic scene is commonly captured by a moving camera (i.e., panning, tilting or hand-held), increasing the task complexity because the scene is observed from different viewpoints. The main challenge is the disambiguation of the camera motion from scene motions, which becomes more difficult as the amount of rigid parts observed decreases. In this talk, We introduce a method to learn a rigidity of a scene from a large collection of dynamic scene data, and directly infer a rigidity mask from two sequential RGB-D images in a supervised manner. With the learned network, we show how we can effectively estimate camera motion and projected scene flow using computed 2D optical flow and the inferred rigidity mask. Through evaluations, we show that our methods can make the scene flow estimation more robust and stable over state-of-the-art methods in challenging dynamic scenes. The expected audiences will include people who are interested in computer vision algorithms, but not limited to any audiences interested in AI and machine learning in general. We'll cover: the motivation behind scene flow estimation, potential applications, how we train two networks for the scene flow estimation, and how we evaluate the algorithm with popular benchmark dataset, SINTEL. We'll also show a new semi-synthetic dataset and its generation method where we mix real video footage with virtually rendered foreground scenes.  Back
 
Keywords:
AI and DL Research, Computer Vision, GTC Silicon Valley 2018 - ID S8798
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Deep Learning for Transportation: Fast Estimation of Travel Times Using Historical Routes
Dmitry Kudinov (Esri Inc.)
During this presentation we will review a deep neural network architecture and its training approaches used for producing high volume of estimations of travel times on a road graph with historical routes and traffic. This includes initial and continu ...Read More
During this presentation we will review a deep neural network architecture and its training approaches used for producing high volume of estimations of travel times on a road graph with historical routes and traffic. This includes initial and continuous online training, finding various sources to produce training data, challenges of quality control, and, of course, the invaluable role of GPU's for computation during both training and inference.  Back
 
Keywords:
AI and DL Research, Product & Building Design, Intelligent Video Analytics and Smart Cities, GIS, Autonomous Vehicles, GTC Silicon Valley 2018 - ID S8156
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Block-Sparse Recurrent Neural Networks
Sharan Narang (Baidu USA), Eric Undersander (Baidu USA)
Recurrent neural networks are used in state-of-the-art models in domains such as speech recognition, machine translation, and language modeling. Sparsity is a technique to reduce compute and memory requirements of deep learning models. Sparse RNNs ar ...Read More
Recurrent neural networks are used in state-of-the-art models in domains such as speech recognition, machine translation, and language modeling. Sparsity is a technique to reduce compute and memory requirements of deep learning models. Sparse RNNs are easier to deploy on devices and high-end server processors. Even though sparse operations need less compute and memory relative to their dense counterparts, the speed-up observed by using sparse operations is less than expected on different hardware platforms. To address this issue, we prune blocks of weights in a layer instead of individual weights. Using these techniques, we can create block-sparse RNNs with sparsity ranging from 80% to 90% with a small loss in accuracy. This technique allows us to reduce the model size by 10x. Additionally, we can prune a larger dense network to recover this loss in accuracy while maintaining high block sparsity and reducing the overall parameter count. Our technique works with a variety of block sizes up to 32x32. Block-sparse RNNs eliminate overheads related to data storage and irregular memory accesses while increasing hardware efficiency compared to unstructured sparsity.  Back
 
Keywords:
AI and DL Research, HPC and AI, GTC Silicon Valley 2018 - ID S8924
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Learning from Limited Data
Tatsuya Harada (University of Tokyo)
Constructing an accurate prediction model from limited data is one of the important tasks in machine learning. We'll introduce unsupervised domain adaptation and a learning method using interclass patterns as a method to construct accurate predictio ...Read More
Constructing an accurate prediction model from limited data is one of the important tasks in machine learning. We'll introduce unsupervised domain adaptation and a learning method using interclass patterns as a method to construct accurate prediction models from limited data. Regarding unsupervised domain adaptation, we use three networks asymmetrically. Two networks are used to label unlabeled target patterns, and one network is trained by the pseudo-labeled patterns to obtain target-discriminative representations. About the learning method using interclass patterns, we generate interclass patterns by mixing two patterns belonging to different classes with a random ratio and train the model to output the mixing ratio form the mixed patterns. Although the algorithm is very simple, the proposed method significantly improves classification performance on sound recognition and image recognition. In addition, we'll briefly introduce various topics, including WebDNN, which our team is working on.  Back
 
Keywords:
AI and DL Research, GTC Silicon Valley 2018 - ID S8786
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Deep Generative Models for Image and Video Creation
Vineeth N Balasubramanian (Indian Institute of Technology (IIT), Hyderabad, India)
We'll focus on recent developments in deep learning-based generative models for image and video creation. The last two to three years have seen an explosive growth in the development of generative adversarial networks, variational autoencoders, and ...Read More
We'll focus on recent developments in deep learning-based generative models for image and video creation. The last two to three years have seen an explosive growth in the development of generative adversarial networks, variational autoencoders, and related autoregressive methods that have been made it possible to automatically generate images and videos, by harnessing the power of GPUs and deep learning libraries. These methods present interesting possibilities in automatic generation of datasets for training machine learning methods, as well as in real-world applications for image and video processing such as morphing, editing, advertising, design, and art. We'll present the technical details of these methods and recent results in various settings.  Back
 
Keywords:
AI and DL Research, Advanced AI Learning Techniques (incl. GANs and NTMs), Video and Image Processing, GTC Silicon Valley 2018 - ID S8784
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Geometry-Aware Learning of Maps for Camera Localization
Jinwei Gu (NVIDIA)
Maps are a key component in image-based camera localization and visual SLAM systems: they are used to establish geometric constraints between images, correct drift in relative pose estimation, and relocalize cameras after lost tracking. The exact def ...Read More
Maps are a key component in image-based camera localization and visual SLAM systems: they are used to establish geometric constraints between images, correct drift in relative pose estimation, and relocalize cameras after lost tracking. The exact definitions of maps, however, are often application-specific and hand-crafted for different scenarios (e.g., 3D landmarks, lines, planes, bags of visual words). We propose to represent maps as a deep neural net called MapNet, which enables learning a data-driven map representation. Unlike prior work on learning maps, MapNet exploits cheap and ubiquitous sensory inputs like visual odometry and GPS in addition to images and fuses them together for camera localization. Geometric constraints expressed by these inputs, which have traditionally been used in bundle adjustment or pose-graph optimization, are formulated as loss terms in MapNet training and also used during inference. In addition to directly improving localization accuracy, this allows us to update the MapNet (i.e., maps) in a self-supervised manner using additional unlabeled video sequences from the scene.  Back
 
Keywords:
AI and DL Research, Autonomous Vehicles, Computer Vision, GTC Silicon Valley 2018 - ID S8792
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Dense Connection Networks for Conversational Speech Recognition
Kyu Han (Capio Inc.), Ian Lane (Carnegie Mellon University)
Densely connected neural networks were originally introduced to avoid the problem of layer-wise vanishing gradients when CNNs are stacked in a very deep fashion, specifically for image recognition tasks. Inspired by these works, we've explored the u ...Read More
Densely connected neural networks were originally introduced to avoid the problem of layer-wise vanishing gradients when CNNs are stacked in a very deep fashion, specifically for image recognition tasks. Inspired by these works, we've explored the use of dense networks connections within LSTM models for the task of automatic speech recognition. By introducing additional connections, to connect (almost) every layer to at least one other layer, we mitigate the vanishing gradient effect between LSTM layers and enable error signals to propagated back to the very first layer during training. In this presentation, we'll present the fundamentals of speech recognition and introduce different neural network model structures that have been shown to be effective for this task. We'll then introduce identity, highway, and dense connections and demonstrate how they improve the performance of these models. We'll evaluate the performance of these models across different datasets, and show that with a lattice-based system combination, densely connected LSTMs significantly contributed to reaching the marks of 5.0% and 9.1% in word error rate (WER) for the Switchboard and CallHome testsets.  Back
 
Keywords:
AI and DL Research, Speech and Language Processing, GTC Silicon Valley 2018 - ID S8903
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Deep Learning Applications in E-Commerce
Krishnendu Chaudhury (Drishti Technologies)
In this talk we will present four applications of deep learning in e-commerce. 1) A deep neural net architecture which has been successfully deployed as a large scale Visual Search and Recommendation system for e-commerce. The deployment has been at ...Read More
In this talk we will present four applications of deep learning in e-commerce. 1) A deep neural net architecture which has been successfully deployed as a large scale Visual Search and Recommendation system for e-commerce. The deployment has been at Flipkart, India's largest e-Commerce vendor, over a catalog of 50M products, supporting 2K queries per second. Our results beat state of the art on the on the Exact Street2Shop dataset. 2) Visual Semantic embedding of e-Commerce products for enhanced searchability and product ranking. 3) Neural Network based click prediction. 4) A novel neural network architecture for demand prediction.  Back
 
Keywords:
AI and DL Research, Deep Learning and AI Frameworks, Consumer Engagement and Personalization, Computer Vision, GTC Silicon Valley 2018 - ID S8684
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Model Architectures and Training Techniques for High-Precision Landmark Localization
Sina Honari (University of Montreal - MILA), Pavlo Molchanov (NVIDIA)
We'll discuss training techniques and deep learning architectures for high-precision landmark localization. In the first part of the session, we'll talk about ReCombinator Networks, which aims at maintaining pixel-level image information ...Read More

We'll discuss training techniques and deep learning architectures for high-precision landmark localization. In the first part of the session, we'll talk about ReCombinator Networks, which aims at maintaining pixel-level image information, for high-accuracy landmark localization. This model combines coarse-to-fine features to first observe global (coarse) image information and then recombines local (fine) information. By using this model, we report SOTA on three facial landmark datasets. This model can be used for other tasks that require pixel-level accuracy (for example, image segmentation, image-to-image translation). In the second part, we'll talk about improving landmark localization in a semi-supervised setting, where less labeled data is provided. Specifically, we consider a scenario where few labeled landmarks are given during training, but lots of weaker labels (for example, face emotions, hand gesture) that are easier to obtain are provided. We'll describe training techniques and model architectures that can leverage weaker labels to improve landmark localization.

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Keywords:
AI and DL Research, Computer Vision, GTC Silicon Valley 2018 - ID S8406
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Learning Robotic Plans from Real-World Demonstrations Using only Randomized Simulated Images
Jonathan Tremblay (NVIDIA)
Using only randomized simulated images, we'll present a system to infer and simply execute a human-readable robotic program after watching a real-world task demonstration. The system is comprised of a series of deep neural network modules, each lear ...Read More
Using only randomized simulated images, we'll present a system to infer and simply execute a human-readable robotic program after watching a real-world task demonstration. The system is comprised of a series of deep neural network modules, each learned entirely in simulation. During training, images are generated in a gaming engine and made transferable to the real world by domain randomization. After training, the system is straightforwardly deployed on a real robot with no retuning of the neural networks and having never previously seen a real image. We demonstrate the system on a Baxter robot performing block tower construction tasks.  Back
 
Keywords:
AI and DL Research, IoT, Robotics & Drones, Robotics & Autonomous Machines, GTC Silicon Valley 2018 - ID S8439
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Can AI Generate Love Advice? Neural Conclusion-Supplement Answer Generation for Non Factoid Questions
Makoto Nakatsuji (NTT Resonant)
Learn how to generate long answers for non-factoid questions in quality assurance community sites by using the encoder-decoder framework. We'll present our novel extension of the encoder-decoder framework, called the ensemble network, that goes beyo ...Read More
Learn how to generate long answers for non-factoid questions in quality assurance community sites by using the encoder-decoder framework. We'll present our novel extension of the encoder-decoder framework, called the ensemble network, that goes beyond a single short sentence. It handles several sentences (i.e. two major sentence types that organize answers for non-factoid questions, conclusion statements, and its supplementary ones) to generate complicated non-factoid answers.  Back
 
Keywords:
AI and DL Research, Speech and Language Processing, GTC Silicon Valley 2018 - ID S8301
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Re3: Realtime Recurrent Regression Networks for Visual Tracking of Generic Objects
Daniel Gordon (University of Washington)
Robust object tracking requires knowledge and understanding of the object being tracked: its appearance, motion, and change over time. A tracker must be able to modify its underlying model and adapt to new observations. We present Re3, a real-ti ...Read More

Robust object tracking requires knowledge and understanding of the object being tracked: its appearance, motion, and change over time. A tracker must be able to modify its underlying model and adapt to new observations. We present Re3, a real-time deep object tracker capable of incorporating temporal information into its model. Rather than focusing on a limited set of objects or training a model at test-time to track a specific instance, we pretrain our generic tracker on a large variety of objects and efficiently update on the fly; Re3 simultaneously tracks and updates the appearance model with a single forward pass. This lightweight model is capable of tracking objects at 150 FPS, while attaining competitive results on challenging benchmarks. We also show that our method handles temporary occlusion better than other comparable trackers using experiments that directly measure performance on sequences with occlusion.

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Keywords:
AI and DL Research, Intelligent Video Analytics and Smart Cities, Autonomous Machines, Computer Vision, Robotics & Autonomous Machines, GTC Silicon Valley 2018 - ID S8298
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Training ImageNet In 15 Minutes With ChainerMN: A Scalable Distributed DL Framework
Keisuke Fukuda (Preferred Networks, Inc.)
We''ll present a multi-node distributed deep learning framework called ChainerMN. Even though GPUs are continuously gaining more computation throughput, it is still very time-consuming to train state-of-the-art deep neural network models. For better ...Read More
We''ll present a multi-node distributed deep learning framework called ChainerMN. Even though GPUs are continuously gaining more computation throughput, it is still very time-consuming to train state-of-the-art deep neural network models. For better scalability and productivity, it is paramount to accelerate the training process by using multiple GPUs. To enable high-performance and flexible distributed training, ChainerMN was developed and built on top of Chainer. We''ll first introduce the basic approaches to distributed deep learning and then explain the design choice, basic usage, and implementation details of Chainer and ChainerMN. To demonstrate the scalability and efficiency of ChainerMN, we''ll discuss the remarkable results from training ResNet-50 classification model on ImageNet database using 1024 Tesla P100 GPUs and our in-house cluster, MN-1.    Back
 
Keywords:
AI and DL Research, NVIDIA Inception Program, Deep Learning and AI Frameworks, HPC and AI, GTC Silicon Valley 2018 - ID S8889
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Towards Theory of AI's Mind
Devi Parikh (Georgia Tech and Facebook AI Research)
To effectively leverage the progress in Artificial Intelligence (AI) to make our lives more productive, it is important for humans and AI to work well together in a team. Traditionally, research has focused primarily on making AI more accurate, and ( ...Read More
To effectively leverage the progress in Artificial Intelligence (AI) to make our lives more productive, it is important for humans and AI to work well together in a team. Traditionally, research has focused primarily on making AI more accurate, and (to a lesser extent) on having it better understand human intentions, tendencies, beliefs, and contexts. The latter involves making AI more human-like and having it develop a theory of our minds. In this talk, I will argue that for human-AI teams to be effective, humans must also develop a Theory of AI''s Mind get to know its strengths, weaknesses, beliefs, and quirks. I will present some (very) initial results in the context of visual question answering and visual dialog where the AI agent is trained to answer natural language questions about images.  Back
 
Keywords:
AI and DL Research, Computer Vision, GTC Silicon Valley 2018 - ID S8560
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Deep Learning for Computational Science
Yang Juntao (NVIDIA)
We''ll review our study of the use of artificial intelligence to augment various domains of computational science in order to improve time to solution for various HPC problems. We''ll discuss the current state-of-the-art approaches and performance ga ...Read More
We''ll review our study of the use of artificial intelligence to augment various domains of computational science in order to improve time to solution for various HPC problems. We''ll discuss the current state-of-the-art approaches and performance gains where applicable. We''ll also investigate current barriers to adoption and consider possible solutions.  Back
 
Keywords:
AI and DL Research, HPC and AI, GTC Silicon Valley 2018 - ID S8242
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Deep Learning for Driver State Sensing
Lex Fridman (MIT)
We''ll explore how deep learning approaches can be used for perceiving and interpreting the driver''s state and behavior during manual, semi-autonomous, and fully-autonomous driving. We''ll cover how convolutional, recurr ...Read More

We''ll explore how deep learning approaches can be used for perceiving and interpreting the driver''s state and behavior during manual, semi-autonomous, and fully-autonomous driving. We''ll cover how convolutional, recurrent, and generative neural networks can be used for applications of glance classification, face recognition, cognitive load estimation, emotion recognition, drowsiness detection, body pose estimation, natural language processing, and activity recognition in a mixture of audio and video data.

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Keywords:
AI and DL Research, Autonomous Vehicles, Autonomous Driving, GTC Silicon Valley 2018 - ID S8626
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Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
Fangchang Ma (Massachusetts Institute of Technology)
Learn how to predict a dense depth image from a sparse set of depth measurements and a single RGB image. This approach can be applied to serve as a plug-in module in simultaneous localization and mapping to convert sparse maps to dense maps, and as a ...Read More
Learn how to predict a dense depth image from a sparse set of depth measurements and a single RGB image. This approach can be applied to serve as a plug-in module in simultaneous localization and mapping to convert sparse maps to dense maps, and as a super-resolution of LiDAR depth data. We''ll describe the performance of our prediction method, explain how to train the depth prediction network, and showcase examples of its applications. Codes and video demonstration are also publicly available. This session is for registrants who are already familiar with basic machine learning techniques.  Back
 
Keywords:
AI and DL Research, Computer Vision, GTC Silicon Valley 2018 - ID S8216
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Additive Learning Framework for Self-Evolving AI
Arpit Baheti (NVIDIA), Sagar Bhokre (NVIDIA)
We''ll present a framework that can learn a compute-intensive deep neural networks (DNNs) task using multiple AI blocks and evolve better confidence by combining estimates. We''ll consider the example of establishing the identity of a user using spee ...Read More
We''ll present a framework that can learn a compute-intensive deep neural networks (DNNs) task using multiple AI blocks and evolve better confidence by combining estimates. We''ll consider the example of establishing the identity of a user using speech and image data. The system consists of two blocks - the AI block and Arbiter block. The AI block uses multiple DNNs (voice-based and image-based DNNs that generate a low confidence estimate initially). These AI blocks assist each other using Arbiter blocks and build confidence, improve accuracy, and learn salient features over time. Arbiter can store recent unacquainted data at run time in noisy and distorted environments and train the AI blocks periodically or on an on-demand basis. This concept could potentially improve the automatic speech recognition capabilities and allow detection of faces even when variable features of faces change with time. The GPU is the ideal choice as the task requires inferencing as well as training on the go.  Back
 
Keywords:
AI and DL Research, Intelligent Video Analytics and Smart Cities, Advanced AI Learning Techniques (incl. GANs and NTMs), GTC Silicon Valley 2018 - ID S8331
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Attention GAN for Fine-Grained Language-to-Image Generation
Pengchuan Zhang (Microsoft Research)
We have long envisioned that machines one day can perform human-like perception, reasoning, and expression across multiple modalities including vision and language, which will augment and transform the ways humans communicate with each other and with ...Read More
We have long envisioned that machines one day can perform human-like perception, reasoning, and expression across multiple modalities including vision and language, which will augment and transform the ways humans communicate with each other and with the real world. With this vision, we''ll introduce the latest work of developing a deep attention GAN for fine-grained language-to-image synthesis. We''ll discuss the open problems behind the task that we''re thrilled to solve, including image and language understanding, joint reasoning across both modalities, and expressing abstract concepts into full imagination, which are of fundamental importance to reaching general intelligence.  Back
 
Keywords:
AI and DL Research, Advanced AI Learning Techniques (incl. GANs and NTMs), GTC Silicon Valley 2018 - ID S8867
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Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
Song Han (Stanford/Google/MIT)
We find 99.9 percent of the gradient exchange in distributed SGD is redundant, and we propose deep gradient compression (DGC) to greatly reduce the communication bandwidth and improve the scalability of distributed training. To preserve accuracy duri ...Read More
We find 99.9 percent of the gradient exchange in distributed SGD is redundant, and we propose deep gradient compression (DGC) to greatly reduce the communication bandwidth and improve the scalability of distributed training. To preserve accuracy during this compression, DGC employs four methods: momentum correction, local gradient clipping, momentum factor masking, and warm-up training. We have applied DGC to image classification, speech recognition, and language modeling with multiple datasets including Cifar10, ImageNet, Penn Treebank, and Librispeech Corpus. In all these scenarios, DGC achieves a gradient compression ratio from 270x to 600x without losing accuracy, cutting the gradient size of ResNet-50 from 97MB to 0.35MB, and for DeepSpeech from 488MB to 0.74MB. DGC enables large-scale distributed training on inexpensive commodity 1Gbps Ethernet and facilitates distributed training on mobile.  Back
 
Keywords:
AI and DL Research, GTC Silicon Valley 2018 - ID S8607
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Deep Learning for Recommender Systems
Justin Basilico (Netflix), Yves Raimond (Netflix)
In this talk, we will survey how Deep Learning methods can be applied to personalization and recommendations. We will cover why standard Deep Learning approaches don''t perform better than typical collaborative filtering techniques. Then ...Read More

In this talk, we will survey how Deep Learning methods can be applied to personalization and recommendations. We will cover why standard Deep Learning approaches don''t perform better than typical collaborative filtering techniques. Then we will survey we will go over recently published research at the intersection of Deep Learning and recommender systems, looking at how they integrate new types of data, explore new models, or change the recommendation problem statement. We will also highlight some of the ways that neural networks are used at Netflix and how we can use GPUs to train recommender systems. Finally, we will highlight promising new directions in this space.

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Keywords:
AI and DL Research, Consumer Engagement and Personalization, Deep Learning and AI, GTC Silicon Valley 2018 - ID S81011
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Efficient Communication Library for Large-Scale Deep-Learning
Minsik Cho (IBM Research)
We''ll talk about the challenges in a large-scale distributed, GPU-based deep learning, and propose an efficient communication algorithm to achieve state-of-the-art scalability. In detail, we''ll explain various ways to speed up GPU-based deep learni ...Read More
We''ll talk about the challenges in a large-scale distributed, GPU-based deep learning, and propose an efficient communication algorithm to achieve state-of-the-art scalability. In detail, we''ll explain various ways to speed up GPU-based deep learning, and motivate the large-scale deep-learning in the performance context. Then, we will state that efficient communication is a grand challenge in the large-scale deep-learning, especially with upcoming more powerful GPUs such as Volta architecture Tesla V100. We''ll present the technical details on a proposed communication algorithm along with the supporting data collected with more than 100 GPUs.  Back
 
Keywords:
AI and DL Research, Deep Learning and AI Frameworks, GTC Silicon Valley 2018 - ID S8479
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Designing Human Centric Spaces with Holodeck and Machine Learning
Cobus Bothma (KPF), Xin Zhang (Kohn Pedersen Fox Associates)
The growth in density of housing in cities like London and New York has resulted in the higher demand for efficient smaller apartments. These designs challenge the use of space and function while trying to ensure the occupants have the perceptio ...Read More

The growth in density of housing in cities like London and New York has resulted in the higher demand for efficient smaller apartments. These designs challenge the use of space and function while trying to ensure the occupants have the perception of a larger space than provided. The process of designing these spaces has always been the responsibility and perception of a handful of designers using 2D and 3D static platforms as part of the overall building design and evaluation, typically constraint by a prescriptive program and functional requirement. A combination of human- and AI-based agents creating and testing these spaces through design and virtual immersive environments (NVIDIA Holodeck) will attempt to ensure the final results are efficient and best fit for human occupancy prior to construction.

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Keywords:
AI and DL Research, Virtual Reality and Augmented Reality, GTC Silicon Valley 2018 - ID S8398
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Learning Steering Bounds for Parallel Autonomy: Handling Ambiguity in End-to-End Driving
Alexander Amini (Massachusetts Institute of Technology)
End-to-end learning is a powerful new strategy for training neural networks from perception to control. While such systems have been shown to perform well for reactionary control, the representation learned is not usable for higher level decision mak ...Read More
End-to-end learning is a powerful new strategy for training neural networks from perception to control. While such systems have been shown to perform well for reactionary control, the representation learned is not usable for higher level decision making, such as navigation. We''ll discuss the latest methodologies for training end-to-end systems for parallel autonomy, and demonstrate some of the shortcomings when such decision making capability is needed.  Back
 
Keywords:
AI and DL Research, Autonomous Vehicles, GTC Silicon Valley 2018 - ID S8605
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Synthetic Data Generation for an All-in-One Driver Monitoring System
Sagar Bhokre (NVIDIA)
Driver monitoring systems are used to detect many driver attributes like gaze, head pose, eye openness, and other features pertaining to attention and assistance. We''ll present a synthetic method of generating data for training DNNs, which caters to ...Read More
Driver monitoring systems are used to detect many driver attributes like gaze, head pose, eye openness, and other features pertaining to attention and assistance. We''ll present a synthetic method of generating data for training DNNs, which caters to the above mentioned features of the subject. We use blender for generating synthetic images, powered by NVIDIA GPUs, which can be scaled to match training needs. Synthetic data generatation allows precise control over data points that are difficult to control in a real environment, like pupil dialation. This approach avoids noisy measurements and results in high accuracy without the need for a high-precision 3D sensor.  Back
 
Keywords:
AI and DL Research, Autonomous Vehicles, Advanced AI Learning Techniques (incl. GANs and NTMs), GTC Silicon Valley 2018 - ID S8324
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Deep Learning For Intelligent Multi-Sensor Analytics
Kyle Muchmore (KickView), David Ohm (KickView)
Go beyond working with a single sensor and enter the realm of Intelligent Multi-Sensor Analytics (IMSA). We''ll introduce concepts and methods for using deep learning with multi-sensor, or heterogenous, data. There are many resources and ...Read More

Go beyond working with a single sensor and enter the realm of Intelligent Multi-Sensor Analytics (IMSA). We''ll introduce concepts and methods for using deep learning with multi-sensor, or heterogenous, data. There are many resources and examples available for learning how to leverage deep learning with public imagery datasets. However, few resources exist to demonstrate how to combine and use these techniques to process multi-sensor data. As an example, we''ll introduce some basic methods for using deep learning to process radio frequency (RF) signals and make it a part of your intelligent video analytics solutions. We''ll also introduce methods for adapting existing deep learning frameworks for multiple sensor signal types (for example, RF, acoustic, and radar). We''ll share multiple use cases and examples for leveraging IMSA in smart city, telecommunications, and security applications.

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Keywords:
AI and DL Research, Intelligent Video Analytics and Smart Cities, Autonomous Machines, GTC Silicon Valley 2018 - ID S8260
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Accelerating Scientific Simulation with Generative Adversarial Networks
Luke de Oliveira (Vai Technologies), Benjamin Nachman (Lawrence Berkeley National Laboratory), Michela Paganini (Yale University)
Many scientific and engineering fields increasingly rely on complex and time consuming computational simulation as part of the modern scientific workflow. In many applications, such as High Energy Particle Physics, Cosmology, Geophysics, and others, ...Read More
Many scientific and engineering fields increasingly rely on complex and time consuming computational simulation as part of the modern scientific workflow. In many applications, such as High Energy Particle Physics, Cosmology, Geophysics, and others, simulations are the computational bottleneck for producing and testing results. We introduce the usage of Generative Adversarial Networks (GAN) as a potential tool for speeding up expensive theoretical models and simulations in scientific and engineering applications, ushering in a new era of deep learning-powered scientific discovery. We will show that using a GAN-based High Energy Physics fast simulator on GPUs can provide speedups of up to 100,000x when compared to traditional simulation software, while retaining high levels of precision. Finally, we will discuss modeling and architectural considerations in this domain with the hope of directly empowering scientists and engineers in other fields to experiment with Generative Adversarial Networks in order to speed up simulation across scientific domains.  Back
 
Keywords:
AI and DL Research, Advanced AI Learning Techniques (incl. GANs and NTMs), HPC and AI, GTC Silicon Valley 2018 - ID S81001
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Deep Reinforcement Learning for Real-World Robotic Manipulation
Tuomas Haarnoja (UC Berkeley)
Deep reinforcement learning (deep RL) has emerged as a promising direction for autonomous acquisition of complex behaviors due to its ability to process complex sensory input and to acquire elaborate behavior skills, using general-purpose neural netw ...Read More
Deep reinforcement learning (deep RL) has emerged as a promising direction for autonomous acquisition of complex behaviors due to its ability to process complex sensory input and to acquire elaborate behavior skills, using general-purpose neural network representations. Since learning expressive function approximators requires large quantities of data, deep RL has been mostly applied to simulated domains, such as video games and simulated robotic locomotion and manipulation tasks, where the data collection can occur faster than real time and be trivially parallelized. We''ll address techniques that have been proposed to enable deep RL for real-world robotics, and discuss how the maximum-entropy principle can be leveraged to reduce the required amount of real-world interaction.  Back
 
Keywords:
AI and DL Research, GTC Silicon Valley 2018 - ID S8603
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Generate Neural Network Automatically with High Accuracy and High Efficiency
Chao Xue (IBM)
Designing neural network architectures are critical for deep learning applications, but it is so complex and depends on AI experts. We''ll demonstrate how you can learn how to construct neural networks automatically without the human intervention. Th ...Read More
Designing neural network architectures are critical for deep learning applications, but it is so complex and depends on AI experts. We''ll demonstrate how you can learn how to construct neural networks automatically without the human intervention. There are two fundamental limiters to the performance of auto-generated neural networks: accuracy and efficiency, which is caused by searching overhead. We''ll also explore new techniques to make auto-generated neural network methods accurate and efficient, including: end-to-end technology to construct neural network within reinforcement learning, adaptive random search and bayesian optimization framework for different AI domains, such as computer vision, IoT acoustics, NLP and finance; using historical knowledge bases to reduce the searching overhead; and scheduling the execution of searching tasks over multiple NVIDIA GPUs to speed up the searching process. Also, we''ll give both the theoretical analysis and experiment results, which show significant improvement of accuracy and substantial reduction of searching time.  Back
 
Keywords:
AI and DL Research, GTC Silicon Valley 2018 - ID S8234
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GPU Accelerated Sequence Learning for Action Recognition
Yemin Shi (Peking University)
We''ll introduce several attempts for modeling the long-term sequence dependence to help improve the action recognition performance. First, we''ll introduce a fused feature of deep and hand-crafted features to prove the complementation between them. ...Read More
We''ll introduce several attempts for modeling the long-term sequence dependence to help improve the action recognition performance. First, we''ll introduce a fused feature of deep and hand-crafted features to prove the complementation between them. We''ll also introduce an attempt of attention model to illustrate the effectiveness of attention mechanism on action recognition. We''ll then introduce shuttleNet, which is a biologically-inspired neural network. Finally, we''ll give some divergent experiments on action recognition to show the potential research direction.  Back
 
Keywords:
AI and DL Research, Computer Vision, Video and Image Processing, GTC Silicon Valley 2018 - ID S8229
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The Future of the In-Car Experience
Abdelrahman Mahmoud (Affectiva), Ashutosh Sanan (Affectiva)
As the race to full autonomy accelerates, the in-cab transportation experience is also being redefined. Future vehicles will sense the passengers'' identities and activities, as well as their cognitive and emotional states, to adapt and ...Read More

As the race to full autonomy accelerates, the in-cab transportation experience is also being redefined. Future vehicles will sense the passengers'' identities and activities, as well as their cognitive and emotional states, to adapt and optimize their experience. AI capable of interpreting what we call "people analytics" captured through their facial and vocal expressions, and aspects of the context that surrounds them will power these advances. We''ll give an overview of our Emotion AI solution, and describe how we employ techniques like deep learning-based spatio-temporal modeling. By combining these techniques with a large-scale dataset, we can develop AI capable of redefining the in-cab experience.

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Keywords:
AI and DL Research, NVIDIA Inception Program, Deep Learning and AI Frameworks, Autonomous Vehicles, GTC Silicon Valley 2018 - ID S8758
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Smart City: Deep Learning Model for Car-Pedestrian Interaction
Zoran Kostic (Columbia University)
In this talk we will discuss the work Columbia University, in partnership with NYC government, is using deep learning and GPUs to develop smart city traffic management facilitating support for navigation/movement of multitude of vehicles (including a ...Read More
In this talk we will discuss the work Columbia University, in partnership with NYC government, is using deep learning and GPUs to develop smart city traffic management facilitating support for navigation/movement of multitude of vehicles (including autonomous cars) in dense urban environments with many pedestrians. We will describe our work in real-time tracking of cars and pedestrians, prediction of movement based on historical observations of the intersection, backed by ultra-low latency wireless communications and edge computing nodes.  Back
 
Keywords:
AI and DL Research, Intelligent Video Analytics and Smart Cities, Autonomous Vehicles, GTC Silicon Valley 2018 - ID S8201
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Differentiable Tree Planning for Deep Reinforcement Learning
Gregory Farquhar (University of Oxford)
We''ll discuss recent research in deep reinforcement learning (RL), with a focus on the application of intuitions, from planning to neural network architectures for deep RL. Planning in complex visual environments has thus far been held back by the d ...Read More
We''ll discuss recent research in deep reinforcement learning (RL), with a focus on the application of intuitions, from planning to neural network architectures for deep RL. Planning in complex visual environments has thus far been held back by the difficulty of learning accurate predictive models. To address this, we embedded a model inside a differentiable, dynamically-constructed tree-planning architecture, so that we identify an effective model when used within that planner. We''ll share our work on developing these architectures, as well as our approaches to various technical obstacles associated with the efficient optimization of deep tree-structured models on GPU.  Back
 
Keywords:
AI and DL Research, Advanced AI Learning Techniques (incl. GANs and NTMs), GTC Silicon Valley 2018 - ID S8787
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Training Deep AutoEncoders for Collaborative Filtering
Oleksii Kuchaiev (NVIDIA)
This session will describe an approach to building personalized recommendations using (very) deep autoencoders. We will explore effects of different activation functions, network depth and novel algorithmic approaches. The model is trained end-to-end ...Read More
This session will describe an approach to building personalized recommendations using (very) deep autoencoders. We will explore effects of different activation functions, network depth and novel algorithmic approaches. The model is trained end-to-end without any layer-wise pre-training and our PyTorch-based code is publicly available.  Back
 
Keywords:
AI and DL Research, Consumer Engagement and Personalization, GTC Silicon Valley 2018 - ID S8212
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GAN Fashion Photo Shoot: Garment to Model Images Using Conditional GANs
Costa Colbert (MAD Street Den, Inc./ VUE.ai)
Learn how VUE.ai''s model generator uses conditional GANs to produce product-specific images suitable for replacing photographs in catalogs. We''ll present networks that generate images of fashion models wearing specific garments, using an image of t ...Read More
Learn how VUE.ai''s model generator uses conditional GANs to produce product-specific images suitable for replacing photographs in catalogs. We''ll present networks that generate images of fashion models wearing specific garments, using an image of the garment as a conditioning variable. Network architecture variants, training, and manipulation of latent variables to control attributes such as model pose, build, or skin color will be addressed.  Back
 
Keywords:
AI and DL Research, NVIDIA Inception Program, Advanced AI Learning Techniques (incl. GANs and NTMs), GTC Silicon Valley 2018 - ID S8776
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Learning Affinity via Spatial Propagation Networks
Sifei Liu (NVIDIA)
We provide a unified framework on learning affinity in pure data-driven fashion using a linear propagation structure. This is a GPU and deep learning friendly pairwise learning module that does not require solving linear equation, iterative inference ...Read More
We provide a unified framework on learning affinity in pure data-driven fashion using a linear propagation structure. This is a GPU and deep learning friendly pairwise learning module that does not require solving linear equation, iterative inferences or manually defined kernels. Specifically, we develop a three-way connection for the linear propagation model, which formulates a sparse transformation matrix, where all elements can be the output from a deep CNN, but results in a dense affinity matrix that effectively models any task-specific pairwise similarity matrix. The spatial propagation network can be applied to many affinity-related tasks, such as image matting, segmentation and colorization, to name a few. Essentially, the model can learn semantically aware affinity relations for high-level vision tasks due to the powerful learning capability of the deep CNN. We validate the framework on the task of refinement for image segmentation boundaries. Experiments on face parsing and semantic segmentation tasks show that the spatial propagation network provides a general, effective, and efficient solution for generating high-quality segmentation results.  Back
 
Keywords:
AI and DL Research, Computer Vision, Video and Image Processing, GTC Silicon Valley 2018 - ID S8312
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Scaling Deep Learning for Immersive User Interfaces
Joel Hestness (Baidu Research)
Deep learning creates advances following a virtuous recipe: model architecture search, creating large training datasets, and scaling computation. Baidu Research''s Silicon Valley AI Lab develops state-of-the-art conversational user interfaces followi ...Read More
Deep learning creates advances following a virtuous recipe: model architecture search, creating large training datasets, and scaling computation. Baidu Research''s Silicon Valley AI Lab develops state-of-the-art conversational user interfaces following this DL recipe. We research new model architectures and features for speech recognition (Deep Speech 3), speech generation (Deep Voice 3), and natural language processing. To deploy these models in impactful products, we want a deep understanding of how recipe components coordinate to drive accuracy improvements. Through large-scale empirical studies, we find intriguing results about how deep learning is likely to scale: As training set size increases, DL model generalization error and model sizes scale as particular power-law relationships. For a fixed dataset size, as model size grows, training time remains roughly constant -- larger models require fewer steps to converge to the same accuracy. These scaling relationships have significant implications on DL research, practice, and systems. They can assist model debugging, setting accuracy targets, and decisions about dataset growth and future computing system design.  Back
 
Keywords:
AI and DL Research, GTC Silicon Valley 2018 - ID S8899
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Synthetic Facial Data for Training Deep Neural Networks
Shalini De Mello (NVIDIA)
Training AI agents that can successfully generalize requires large amounts of diverse labeled training data. Collecting and labeling data is a significant cost in the development of AI applications, which, in some cases, may not even be feasib ...Read More
Training AI agents that can successfully generalize requires large amounts of diverse labeled training data. Collecting and labeling data is a significant cost in the development of AI applications, which, in some cases, may not even be feasible. We'll describe computer graphics facial models that we are developing to generate large labeled synthetic facial data for training deep neural networks. Facial analysis is central to many vision applications that involve human-computer interaction, including robotics, autonomous cars, rehabilitation, and extended usability. Generating and animating human faces with high realism is a well-studied problem in computer graphics; however, very few computer vision AI techniques take advantage of rendered facial data to augment or replace manually collected training data. We'll share key insights of how we successfully use synthetic facial data for training facial analysis classifiers. We'll also demonstrate many sub-tasks on which synthetic data helps to significantly improve accuracy and reduces the need for manual data collection.
 
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Keywords:
AI and DL Research, Intelligent Video Analytics and Smart Cities, GTC Silicon Valley 2018 - ID S8794
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De Novo Drug Design using Artificial Intelligence
Olexandr Isayev (University of North Carolina)
We propose a novel computational strategy based on deep and reinforcement learning techniques for de-novo design of molecules with desired properties. This strategy integrates two deep neural networks generative and predictive to generate novel c ...Read More
We propose a novel computational strategy based on deep and reinforcement learning techniques for de-novo design of molecules with desired properties. This strategy integrates two deep neural networks generative and predictive to generate novel chemical structures with the desired properties. In the first phase of the method, generative and predictive models are separately trained with supervised learning algorithms. In the second phase, both models are jointly trained with reinforcement learning approach to bias newly generated chemical structures towards those with desired physical and biological properties. In this proof-of-concept study, we have employed this strategy to design chemical libraries biased toward compounds with either maximal, minimal, or specific range of physical properties, such as melting point and hydrophobicity, as well as to develop novel putative inhibitors of JAK2. This new approach can find a general use for generating targeted chemical libraries optimized for a single desired property or multiple properties.  Back
 
Keywords:
AI and DL Research, Computational Biology and Chemistry, GTC Silicon Valley 2018 - ID S8254
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Towards Learning to Imagine Videos with Controlled Content
Sergey Tulyakov (Snap Inc.)
We discuss one of the first attempts to teach computers to imagine or generate videos with controlled content using deep learning generative modeling techniques. To this end, we assume visual information in a natural video can be decomposed into two ...Read More
We discuss one of the first attempts to teach computers to imagine or generate videos with controlled content using deep learning generative modeling techniques. To this end, we assume visual information in a natural video can be decomposed into two major components: content and motion. While content encodes the objects present in the video, motion encodes the object dynamics. Based on this prior, we propose the motion and content decomposed generative adversarial network (MoCoGAN) framework for video generation. The proposed framework generates a video clip by sequentially mapping random noise vectors to video frames. We divide a random noise vector into content and motion parts. By controlling these parts we generate both the content of the video and the action that is being performed. We perform quantitative and qualitative analysis on several video datasets, including artificial shape motion, facial expression, and tai-chi videos.  Back
 
Keywords:
AI and DL Research, Advanced AI Learning Techniques (incl. GANs and NTMs), Computer Vision, GTC Silicon Valley 2018 - ID S8477
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Quick and Easy DL Workflow Proof of Concept
Alec Gunny (NVIDIA), Kenneth Hester (NVIDIA), Jeffrey Weiss (NVIDIA)
Spin up a deep learning (DL) proof-of-concept on a budget. We'll walk you through a DL workflow in the cloud leveraging DIGITS, then download a trained model, and run inference on a Jetson TX2. This session considers multiple options such as Nimbix, ...Read More
Spin up a deep learning (DL) proof-of-concept on a budget. We'll walk you through a DL workflow in the cloud leveraging DIGITS, then download a trained model, and run inference on a Jetson TX2. This session considers multiple options such as Nimbix, AMI, and NGC on Tesla P100, Tesla V100, and NVIDIA DGX-1 servers. This tutorial will be a combination of lecture, live demos, and detailed instructions.  Back
 
Keywords:
AI and DL Research, Accelerated Analytics, GTC Silicon Valley 2018 - ID S8286
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Fully Context-Aware Video Prediction
Wonmin Byeon (NVIDIA)
We'll discuss the development of a novel model for video prediction and analysis -- the parallel multi-dimensional long short-term memory (PMD-LSTM). PMD-LSTM is a general model for learning from higher dimensional data such as images, videos, and b ...Read More
We'll discuss the development of a novel model for video prediction and analysis -- the parallel multi-dimensional long short-term memory (PMD-LSTM). PMD-LSTM is a general model for learning from higher dimensional data such as images, videos, and biomedical scans. It is an extension of the popular LSTM recurrent neural networks to higher dimensional data with a rearrangement of the recurrent connections to dramatically increase parallelism. This gives the network the ability to compactly model the effect of long-range context in each layer, unlike convolutional networks, which need several layers to cover a larger input context. We'll discuss the blind spot problem in recent work on video prediction, and show how PMD-LSTM based models are fully context-aware for each predicted pixel. These models outperform comparatively complex state-of-the-art approaches significantly in a variety of challenging video prediction scenarios such as car driving, human motion, and diverse human actions.  Back
 
Keywords:
AI and DL Research, NVIDIA Inception Program, Computer Vision, GTC Silicon Valley 2018 - ID S8713
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SpaceNet: Accelerating Automated Feature Extraction for Satellite Imagery - Two years, Four Competitions in the Making
Todd M. Bacastow (Radiant Solutions), David Lindenbaum (CosmiQ Works, and IQT Lab)
We'll present the results of the SpaceNet 2017-2018 Challenge, preview future SpaceNet Challenges, and how developers can generally access open labeled satellite image training data through SpaceNet on AWS. To date, three SpaceNet Challenges ha ...Read More
We'll present the results of the SpaceNet 2017-2018 Challenge, preview future SpaceNet Challenges, and how developers can generally access open labeled satellite image training data through SpaceNet on AWS. To date, three SpaceNet Challenges have been designed to apply computer vision techniques to satellite imagery which examine building footprint extraction, road network extraction, and off-nadir object detection. SpaceNet on AWS is an online repository of openly available satellite imagery, co-registered map data to train algorithms for developers and data scientists to access for research. This first-of-its-kind open innovation project for the geospatial industry launched in August 2016 as a collaboration between AWS, CosmiQ Works, DigitalGlobe, and NVIDIA. The SpaceNet Roads Challenge, launching in November, builds on labeled training datasets consisting of building footprints across Khartoum, Las Vegas, Paris, and Shanghai by providing over 8,000 km of mapped road networks. It uses a novel metric motivated by graph theory concepts that focused competitors on routing rather than just static road pixel identification.  Back
 
Keywords:
AI and DL Research, GIS, GTC Silicon Valley 2018 - ID S8553
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Training Neural Networks with Mixed Precision: Theory and Practice
Paulius Micikevicius (NVIDIA)
We'll cover the theory and practice for training DNNs with Tensor Cores, introduced for AI processing with the Volta GPU architecture. Tensor Cores provide up to 120 TFlops throughput, mixing operations on IEEE half- and single-precision floats. In ...Read More
We'll cover the theory and practice for training DNNs with Tensor Cores, introduced for AI processing with the Volta GPU architecture. Tensor Cores provide up to 120 TFlops throughput, mixing operations on IEEE half- and single-precision floats. In the theory portion of the talk, we'll review the half-precision format, values that arise in DNN computations, and techniques that maximize utilization of fp16 format by these values. Techniques include loss-scaling, master weights, and choosing the proper precision for a given operation. In the practice portion of this talk, we'll survey various models that have been trained in mixed precision, matching the accuracy of fp32 training sessions while using the same hyperparameters. Models include various architectures (feed forward, recurrent, generative) as well as cover diverse tasks (image, speech, and language processing). We'll also provide network design and training guidelines to maximize speed when using Tensor Cores.  Back
 
Keywords:
AI and DL Research, Algorithms and Numerical Techniques, GTC Silicon Valley 2018 - ID S8923
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Deep Learning Applications in Text and Graphics at NVIDIA
Bryan Catanzaro (NVIDIA)
At NVIDIA, we're busy applying deep learning to diverse problems, and this talk will give an overview of a few of these applications. We'll discuss our resume matching system, which helps match candidates to job openings at NVIDIA, as well as an op ...Read More
At NVIDIA, we're busy applying deep learning to diverse problems, and this talk will give an overview of a few of these applications. We'll discuss our resume matching system, which helps match candidates to job openings at NVIDIA, as well as an open-source sentiment analysis project trained on unsupervised text that is improving our marketing capabilities. We'll discuss a blind image quality metric that we're using to lower the cost of raytracing photorealistic graphics, and a generative model that we've built to create realistic graphics from simplistic sketches.  Back
 
Keywords:
AI and DL Research, GTC Silicon Valley 2018 - ID S8672
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Unleashing the Imagination: Combining systems+software innovation with GPUs to create new capabilities
Hillery Hunter (IBM)
AI is one of the most rapidly-evolving areas of computer science today and datascientists are constantly pushing the boundaries of the possible -- wanting to explore new data types, new algorithms, and diverse and heterogenous models. In this talk we ...Read More
AI is one of the most rapidly-evolving areas of computer science today and datascientists are constantly pushing the boundaries of the possible -- wanting to explore new data types, new algorithms, and diverse and heterogenous models. In this talk we'll explore two key productivity factors for datascience -- first, speed and the ability to explore many models and sets of data quickly; and second, ability to explore broad types of models, incorporating both machine learning and deep learning. We will talk about results of 40x and 50x productivity through system+software co-design and novel algorithms which leverage Power Systems and GPUs for both deep learning and key areas of classical machine learning. System+software co-design and co-optimization can result in dramatic efficiency improvements, enable creation of large models, exploration of large datasets, and realize productivity gains for datascientists, freeing them up to focus on the fundamental science of deep and machine learning -- gaining accuracy, functionality, and generalizability of their models.  Back
 
Keywords:
AI and DL Research, GTC Silicon Valley 2018 - ID S81025
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Overcoming Missing Modalities in Remote Sensing
Benjamin Bischke (German Research Center for Artificial Intelligence (DFKI)), Damian Borth (German Research Center for Artificial Intelligence (DFKI))
Recent advances in earth observation are opening up a new exciting area for exploration of satellite image data. We'll teach you how to analyse this new data source with deep neural networks. Focusing on emergency response, you will learn how to app ...Read More
Recent advances in earth observation are opening up a new exciting area for exploration of satellite image data. We'll teach you how to analyse this new data source with deep neural networks. Focusing on emergency response, you will learn how to apply deep neural networks for semantic segmentation on satellite imagery. We will specifically focus on multimodal segmentation and the challenge of overcoming missing modality information during inference time. It is assumed that registrants are already familiar with fundamentals of deep neural networks.  Back
 
Keywords:
AI and DL Research, Advanced AI Learning Techniques (incl. GANs and NTMs), GTC Silicon Valley 2018 - ID S8596
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Adaptive Ray Tracing Rendering Powered by Deep Learning
Andrew Tao (NVIDIA), Carsten Waechter (NVIDIA)
This session will present a proof of concept where a deep neural network was trained with pairs of Iray ray traced images (one arbitrary ray tracing iteration number and one fully converged image) and theirs structural similarity index (SSIM). Origin ...Read More
This session will present a proof of concept where a deep neural network was trained with pairs of Iray ray traced images (one arbitrary ray tracing iteration number and one fully converged image) and theirs structural similarity index (SSIM). Originally thought as a method for measuring the similarity between two images, SSIM index can also be viewed as a quality measure versus a reference image or, in our case, as a ray tracing rendering progress. The DNN can now from any render iteration of arbitrary scene infer a rendering progress estimator but also provides heat map pictures of the scenes that can be used for adaptive rendering, focusing ray tracing engine power on appropriate zones.  Back
 
Keywords:
AI and DL Research, Graphics and AI, Rendering and Ray Tracing, GTC Silicon Valley 2018 - ID S8788
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SSD++ Boosting Performance of Single-Shot MultiBox Detection Using Convolution Autoencoders
Vijay Gabale (Huew)
We'll showcase how you can apply a wealth of unlabeled image data to significantly improve accuracy and speed of single-shot object-detection (SSD) techniques. Our approach, SSD++, advances the state-of-the-art of single shot multibox-based object d ...Read More
We'll showcase how you can apply a wealth of unlabeled image data to significantly improve accuracy and speed of single-shot object-detection (SSD) techniques. Our approach, SSD++, advances the state-of-the-art of single shot multibox-based object detectors (such as SSD, YOLO) by employing a novel combination of convolution-deconvolution networks to learn robust feature maps, thus making use of unlabeled dataset, and the fresh approach to have confluence of convolution and deconvolution features to combine generic as well as semantically rich feature maps. As a result, SSD++ drastically reduces the requirement of labeled datasets, works on low-end GPUs, identifies small as well as large objects with high fidelity, and speeds up inference process by decreasing the requirement of default boxes. SSD++ achieves state-of-the-art results on PASCAL VOC and MS COCO datasets. Through ablation study, we'll explain the effectiveness of different components of our architecture that help us achieve improved accuracy on the above datasets. We'll further show a case study of SSD++ to identify shoppable objects in fashion, home decor, and food industry from images in the wild.  Back
 
Keywords:
AI and DL Research, NVIDIA Inception Program, Computer Vision, Video and Image Processing, GTC Silicon Valley 2018 - ID S8159
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Deep Learning for Dialogue Systems
Yun-Nung (Vivian) Chen (National Taiwan University)
Learn how to apply deep learning technologies for building robust and scalable dialogue systems with deeper understanding of the classic pipelines as well as detailed knowledge on the benchmark of the models of the prior work. We'll start with an ov ...Read More
Learn how to apply deep learning technologies for building robust and scalable dialogue systems with deeper understanding of the classic pipelines as well as detailed knowledge on the benchmark of the models of the prior work. We'll start with an overview of the dialogue research and allow the audience to dive deep into the state-of-the-art work about neural-based language understanding, dialogue management, and language generation towards end-to-end neural dialogue systems.  Back
 
Keywords:
AI and DL Research, Speech and Language Processing, GTC Silicon Valley 2018 - ID S8542
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IamNN: Iterative and Adaptive Mobile Neural Network for Efficient Image Classification
Pavlo Molchanov (NVIDIA)
Deep residual networks (ResNets) made a recent breakthrough in deep learning. The core idea of ResNets is to have shortcut connections between layers that allow the network to be much deeper while still being easy to optimize avoiding vanishing ...Read More

Deep residual networks (ResNets) made a recent breakthrough in deep learning. The core idea of ResNets is to have shortcut connections between layers that allow the network to be much deeper while still being easy to optimize avoiding vanishing gradients. These shortcut connections have interesting properties that make ResNets behave differently from other typical network architectures. In this talk we will use these properties to design a network based on a ResNet but with parameter sharing and adaptive computation time, we call it IamNN. The resulting network is much smaller than the original network and can adapt the computational cost to the complexity of the input image. During this talk we will provide an overview of ways to design compact networks, give an overview of ResNets properties and discuss how they can be used to design compact dense network with only 5M parameters for ImageNet classification.

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Keywords:
AI and DL Research, GTC Silicon Valley 2018 - ID S8456
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Multimodal Memory Modelling for Video Captioning
Yan Huang (Institute of Automation, Chinese Academy of Sciences)
This talk presents a novel framework named multimodal memory model for video captioning, which builds a visual and textual shared memory to model the long-term visual-textual dependency and further guide visual attention on described visual targets t ...Read More
This talk presents a novel framework named multimodal memory model for video captioning, which builds a visual and textual shared memory to model the long-term visual-textual dependency and further guide visual attention on described visual targets to solve visual-textual alignments. Video captioning which automatically translates video clips into natural language sentences is a very important task in computer vision. By virtue of recent deep learning technologies, video captioning has made great progress. However, learning an effective mapping from the visual sequence space to the language space is still a challenging problem due to the long-term multimodal dependency modelling and semantic misalignment. Inspired by the facts that memory modelling poses potential advantages to long-term sequential problems and working memory is the key factor of visual attention, the proposed model attaches an external memory to store and retrieve both visual and textual contents by interacting with video and sentence with multiple read and write operations.  Back
 
Keywords:
AI and DL Research, Computer Vision, Video and Image Processing, GTC Silicon Valley 2018 - ID S8311
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Getting Started with Tensorflow on GPUs
Hans Hyttsten (Google)
Want to get started using TensorFlow together with GPUs? Then come to this session, where we will cover the TensorFlow APIs you should use to define and train your models, and the best practices for distributing the training workloads to multipl ...Read More

Want to get started using TensorFlow together with GPUs? Then come to this session, where we will cover the TensorFlow APIs you should use to define and train your models, and the best practices for distributing the training workloads to multiple GPUs. We will also look at the underlying reasons why are GPUs are so great to use for Machine Learning workloads?

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Keywords:
AI and DL Research, Deep Learning and AI, Developer Talk, GTC Silicon Valley 2018 - ID S8946
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Sim2Real Visual Robotic Servoing for Navigation and Manipulation via Deep Reinforcement Learning
Fereshteh Sadeghi (University of Washington)
Humans are remarkably proficient at controlling their limbs and tools from a wide range of viewpoints, diverse environments and in the presence of distractors. In robotics, this ability is referred to as visual servoing. Standard visual servoing appr ...Read More
Humans are remarkably proficient at controlling their limbs and tools from a wide range of viewpoints, diverse environments and in the presence of distractors. In robotics, this ability is referred to as visual servoing. Standard visual servoing approaches have limited generalization as they typically rely on manually designed features and calibrated camera. We exhibit generalizable visual servoing in the context of robotic manipulation and navigation tasks learned through visual feedback and by deep reinforcement learning (RL) without needing any calibrated setup. By highly randomizing our simulator, we train policies that generalize to novel environments and also to the challenging real world scenarios. Our domain randomization technique addresses the high sample complexity of deep RL, avoids the dangers of trial-and-error and also provides us with the liberty to learn recurrent vision-based policies for highly diverse tasks where capturing sufficient real robot data is impractical. An example of such scenario is learning view-invariant robotic policies which leads into learning physical embodiment and self-calibration purely through visual feedback.  Back
 
Keywords:
AI and DL Research, IoT, Robotics & Drones, Robotics & Autonomous Machines, GTC Silicon Valley 2018 - ID S8955
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Investigating Data Augmentation Strategies for Advancing Deep Learning Training
Winston Hsu (National Taiwan University)
We saw the huge success of the deep learning paradigm and the superhuman capability in numerous benchmarks in image, video, audio, or text. However, it poses huge challenges as adopting the methods in industrial applications (mainly due to the lack o ...Read More
We saw the huge success of the deep learning paradigm and the superhuman capability in numerous benchmarks in image, video, audio, or text. However, it poses huge challenges as adopting the methods in industrial applications (mainly due to the lack of quality tracking data) as the neural networks consume enormous parameters and require relatively huge quality training data. We'll aim for investigating the "data augmentation" strategies increasing quality training data for robust inference across different learning problems mainly in image, video, 3D, and IoT data streams. We'll first quantify the importance of training data for deep neural networks then review numerous strategies, such as crawling from the web, utilizing generative models, 3D computer graphics, augmented reality, engagement in social media, gaming, etc. We'll compare the effectiveness among the diverse strategies. As generally taking the data from other domains, we also need to deal with the cross-domain learning problem. We'll provide detailed insights from our recent work published in top conferences (e.g., CVPR, ICCV, AAAI, etc.) and those cases in industrial applications.  Back
 
Keywords:
AI and DL Research, Advanced AI Learning Techniques (incl. GANs and NTMs), GTC Silicon Valley 2018 - ID S8391
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Tensor Layers for Compression of Deep Learning Networks
Cristopher Cecka (NVIDIA)
We'll review recent efforts to compress fully connected layers in machine learning via tensor networks, including the Tensor Train format, the Tensor Contraction Layer, the Tensor Regression Layer, and a Tensor Ring decomposition. These decompositio ...Read More
We'll review recent efforts to compress fully connected layers in machine learning via tensor networks, including the Tensor Train format, the Tensor Contraction Layer, the Tensor Regression Layer, and a Tensor Ring decomposition. These decompositions, in supplementing or replacing fully connected layers, are shown to dramatically reduce the number of parameters required by the network without resorting to sparsity and without loss in error. We've shown 55-80 percent compression of the entire network with less than one percent loss in accuracy. These Tensor layers can be used in end-to-end training, fine-tuning, and transfer-learning by initializing the decomposition with a pre-trained fully connected layer. Furthermore, because the forward and backward passes of the network rely on dense Tensor contractions, we show that these methods retain high computational intensity and can be efficiently evaluated on GPUs.  Back
 
Keywords:
AI and DL Research, Algorithms and Numerical Techniques, HPC and AI, GTC Silicon Valley 2018 - ID S8807
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Accelerating Cancer Research with Deep Learning
Fernanda Foertter (NVIDIA)
The Department of Energy (DOE) entered into a partnership with the National Cancer Institute (NCI) of the National Institutes of Health (NIH) to accelerate cancer research. This "Cancer Moonshot" aims to tackle three main objectives: better ...Read More
The Department of Energy (DOE) entered into a partnership with the National Cancer Institute (NCI) of the National Institutes of Health (NIH) to accelerate cancer research. This "Cancer Moonshot" aims to tackle three main objectives: better understand the mechanisms of cancer, use large amounts of diverse medical data for predictive models, and enable precision medicine by providing guidance for treatment to individual patients. Leveraging the compute expertise of DOE in high performance computing (HPC) and new methods for deep learning in artificial intelligence, this HPC+AI approach aims to create a single scalable deep neural network code called CANDLE (CANcer Distributed Learning Environment) that will be used to address all three challenges. This talk aims to give an overview of the project and highlight how GPU accelerated systems in the DOE ecosystem, Summit and Sierra, have contributed to the project.  Back
 
Keywords:
AI and DL Research, HPC and AI, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S81033
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Multi-Resolution 3D-Convolutional Neural Network for Object Recognition
Sambit Ghadai (Iowa State University), Adarsh Krishnamurthy (Iowa State University)
Voxelized representation of 3D objects is commonly used for training 3D-Convolutional Neural Networks for object detection and classification. However, high-resolution voxelization of CAD models are memory intensive and hence, it is not possible to l ...Read More
Voxelized representation of 3D objects is commonly used for training 3D-Convolutional Neural Networks for object detection and classification. However, high-resolution voxelization of CAD models are memory intensive and hence, it is not possible to load multiple models in the GPU for training. We have developed a GPU-accelerated voxelization technique that generates multi-level voxel grids of 3D objects. Instead of creating a single high-resolution voxel grid for the whole object, this technique generates selective region-based high-resolution voxel grids to represent detailed features in the object. We have also developed a multi-resolution 3D-Convolutional Neural Network that uses this hybrid voxelization for accurate object recognition and classification.  Back
 
Keywords:
AI and DL Research, Industrial Inspection, Computer Vision, GTC Silicon Valley 2018 - ID S8389
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High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
Ting-Chun Wang (NVIDIA)
We'll present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks. Conditional GANs have enabled a variety of applications, but the results are often limited ...Read More
We'll present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks. Conditional GANs have enabled a variety of applications, but the results are often limited to low-res and still far from realistic. We'll show that we're capable of generating 2048x1024 visually appealing results with a novel adversarial loss, as well as new multi-scale generator and discriminator architectures. Furthermore, we extend our framework to interactive visual manipulation with two additional features. First, we incorporate object instance segmentation information, which enables object manipulations such as removing/adding objects and changing the object category. Second, we propose a method to generate diverse results given the same input, allowing users to edit the object appearance interactively. Human opinion studies demonstrate that our method significantly outperforms existing methods, advancing both the quality and the resolution of deep image synthesis and editing.  Back
 
Keywords:
AI and DL Research, Graphics and AI, GTC Silicon Valley 2018 - ID S8918
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GPU Performance Testing and PowerAI on IBM Cloud (Presented by IBM Cloud)
Alex Hudak (IBM), Brian Wan (IBM)
In this session, you will learn about the latest IBM PowerAI solution, IBM Cloud GPU offerings and see a price-performance comparison, with supporting data, on the number of CPUs required to optimize GPU performance. We've also aggregated extensive ...Read More
In this session, you will learn about the latest IBM PowerAI solution, IBM Cloud GPU offerings and see a price-performance comparison, with supporting data, on the number of CPUs required to optimize GPU performance. We've also aggregated extensive test data to determine general best practices such as half-precision deep learning advantages on the Tesla V100 and the implications of neural-network model variable distribution and gradient aggregation techniques on your performance results. Join us to see why NVIDIA GPUs on IBM Cloud offer superior results.  Back
 
Keywords:
AI and DL Research, Accelerated Analytics, GTC Silicon Valley 2018 - ID S81013
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AI for Accelerated Analytics
Presentation
Media
Accelerating Cyber Threat Detection with GPUs
Josh Patterson (NVIDIA)
Analyzing vast amounts of enterprise cyber security data to find threats can be cumbersome. Cyber threat detection is also a continuous task, and because of financial pressure, companies have to find optimized solutions for this volume of data. We'l ...Read More
Analyzing vast amounts of enterprise cyber security data to find threats can be cumbersome. Cyber threat detection is also a continuous task, and because of financial pressure, companies have to find optimized solutions for this volume of data. We'll discuss the evolution of big data architectures used for cyber defense and how GPUs are allowing enterprises to efficiently improve threat detection. We'll discuss (1) briefly the evolution of traditional platforms to lambda architectures and ultimately GPU-accelerated solutions; (2) current GPU-accelerated database, analysis tools, and visualization technologies (such as MapD, BlazingDB, H2O.ai, Anaconda and Graphistry), and discuss the problems they solve; (3) the need to move beyond traditional rule based indicators of compromise and use a combination of machine learning, graph analytics, and deep learning to improve threat detection; and finally (4) our future plans to continue to advance GPU accelerated cyber security R&D as well as the GPU Open Analytics Initiative.  Back
 
Keywords:
AI for Accelerated Analytics, Deep Learning and AI, GTC Washington D.C. 2017 - ID DC7111
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Making Deep Learning Scale: Defense Applications
William Rorrer (Harris)
We'll cover research and development Harris has performed in the application of deep learning to a wide range of geospatial intelligence (GEOINT) problems. As the DoD's urgency for adoption of artificial intelligence and machine learning continues ...Read More
We'll cover research and development Harris has performed in the application of deep learning to a wide range of geospatial intelligence (GEOINT) problems. As the DoD's urgency for adoption of artificial intelligence and machine learning continues to grow, Harris has been investing in automating and scaling processes for designing, training, and deploying deep learning based algorithms for rapid insertion into DoD environments and workflows. Mass production of deep learning algorithms for GEOINT is within reach.  Back
 
Keywords:
AI for Accelerated Analytics, Deep Learning and AI, GTC Washington D.C. 2017 - ID DC7119
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Advanced Analytics and Machine Learning with Geospatial Data: A World of Possibilities
Amit Vij (Kinetica)
As data sources such as sensors, social media, mobile, cloud, and machine logs become pervasive within the enterprise and the 4Vs of data managed by modern business applications increase exponentially, organizations need faster and better visualizati ...Read More
As data sources such as sensors, social media, mobile, cloud, and machine logs become pervasive within the enterprise and the 4Vs of data managed by modern business applications increase exponentially, organizations need faster and better visualizations to explore data at scale and discover game-changing insights. Organizations are increasingly adopting modern technologies such as GPUs to interactively visualize billions of data elements in real-time and take faster business decisions. You'll learn how you can augment your existing business applications and visualization capabilities with GPU-accelerated rendering of maps and accompanying dashboards for spatial awareness and location-based analytics, and how GPU's massive parallelization minimizes the need for data preparation and sampling and delivers brute force capabilities to interactively visualize the fast-moving location and time-series data such as trading, traffic, social media, and vehicle telematics at scale and with speed.  Back
 
Keywords:
AI for Accelerated Analytics, Geospatial Intelligence, GTC Washington D.C. 2017 - ID DC7139
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A Sparse Dynamic Graph and Matrix Data Structure
Oded Green (Georgia Tech)
Sparse data computations are ubiquitous in science and engineering. Two widely used applications requiring sparse data computations are graph algorithms and linear algebra operations such as sparse matrix-vector multiplication (SpMV). In contrast to ...Read More
Sparse data computations are ubiquitous in science and engineering. Two widely used applications requiring sparse data computations are graph algorithms and linear algebra operations such as sparse matrix-vector multiplication (SpMV). In contrast to their dense data counterparts, sparse-data computations have less locality and more irregularity in their execution -- making them significantly more challenging to optimize. While there are several existing formats for sparse data representations, most of these are restricted to static datasets. We'll show a new data structure called Hornet for dynamic graphs and matrices that scales to extremely large graphs. Hornet can be used for both static and dynamic graph-based problems.  Back
 
Keywords:
AI for Accelerated Analytics, GTC Washington D.C. 2017 - ID DC7161
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Fast Item Response Theory (IRT) Model Estimation by Using GPUs
Lei Chen (Educational Testing Service (ETS))
Learn how to use GPUs to speed up the statistical inference when applying Item Response Theory (IRT) models, which describe the statistical relationships among students' test performances, their latent abilities, and test questions' difficulty leve ...Read More
Learn how to use GPUs to speed up the statistical inference when applying Item Response Theory (IRT) models, which describe the statistical relationships among students' test performances, their latent abilities, and test questions' difficulty levels. IRT has been acting as the cornerstone for many education applications, for example, adaptive computer-based assessments and personalized learnings. How to quickly estimate a large number of parameters of IRT models is a challenging task, especially when facing large-sized educational datasets. We'll introduce how to use a modern probabilistic modeling toolkit, Edward, which uses TensorFlow as its backend for efficiently estimating IRT parameters. Compared to CPUs, we found that GPUs can make IRT parameter-estimation four times faster.  Back
 
Keywords:
AI for Accelerated Analytics, Leadership in AI, GTC Washington D.C. 2017 - ID DC7176
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World's Fastest Machine Learning With GPUs
Jon Mckinney (H2O.ai)
Deep learning algorithms have benefited greatly from the recent performance gains of GPUs. However, it has been unclear whether GPUs can speed up machine learning algorithms such as generalized linear modeling, random forests, gradient boosting machi ...Read More
Deep learning algorithms have benefited greatly from the recent performance gains of GPUs. However, it has been unclear whether GPUs can speed up machine learning algorithms such as generalized linear modeling, random forests, gradient boosting machines, and clustering. H2O.ai, the leading open source AI company, is bringing the best-of-breed data science and machine learning algorithms to GPUs. We'll introduce H2O4GPU, a fully featured machine learning library that is optimized for GPUs with a robust Python API that is a drop-dead replacement for scikit-learn. We'll demonstrate benchmarks for the most common algorithms relevant to enterprise AI and showcase performance gains as compared to running on CPUs.  Back
 
Keywords:
AI for Accelerated Analytics, Finance, GTC Washington D.C. 2017 - ID DC7213
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Harnessing Machine Learning to Enable Global Discovery at Scale
Mikel Rodriguez (MITRE)
There has been an explosion in the number of commercially available satellite images produced every day. The democratization of space technology has catalyzed a revolution of the commercial space industry, which is now rapidly transforming from one c ...Read More
There has been an explosion in the number of commercially available satellite images produced every day. The democratization of space technology has catalyzed a revolution of the commercial space industry, which is now rapidly transforming from one company imaging five million km2 a day to around 10 companies imaging 200 million km2 a day; and from constellations of a handful of satellites to constellations of hundreds of satellites. Today, the number of images being generated by this rapidly evolving commercial space industry far exceeds human scales. We'll explore how we can harness machine learning to enable global discovery at scale. Fueled by advancements in machine learning algorithms, GPUs, and the availability of labeled datasets, we have dramatically improved our ability to extract insight from this explosion of data to understand changes across the globe.  Back
 
Keywords:
AI for Accelerated Analytics, GTC Washington D.C. 2017 - ID DC7253
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AI for Gaming
Presentation
Media
The Real-Time Revolution
Adam Myhill (Unity)
GPU accelerated creative development platforms are no longer just for games, they're revolutionizing areas from film to automotive. See how Unity is being used to enable unheard-of levels of productivity and create even deeper collaboration between ...Read More
GPU accelerated creative development platforms are no longer just for games, they're revolutionizing areas from film to automotive. See how Unity is being used to enable unheard-of levels of productivity and create even deeper collaboration between teams.  Back
 
Keywords:
AI for Gaming, Graphics and AI, Real-Time Graphics, GTC Silicon Valley 2018 - ID S81010
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Playing FPS Games with Deep Reinforcement Learning
Devendra Singh Chaplot (Carnegie Mellon University)
Advances in deep reinforcement learning have allowed autonomous agents to perform well on Atari games, often outperforming humans, using only raw pixels to make their decisions. However, most of these games take place in 2D environments that are full ...Read More
Advances in deep reinforcement learning have allowed autonomous agents to perform well on Atari games, often outperforming humans, using only raw pixels to make their decisions. However, most of these games take place in 2D environments that are fully observable to the agent. We present the first architecture to tackle 3D environments in first-person shooter games that involve partially observable states. Typically, deep reinforcement learning methods only utilize visual input for training. We present a method to augment these models to exploit game feature information, such as the presence of enemies or items, during the training phase. Our model is trained to simultaneously learn these features along with minimizing a Q-learning objective, which is shown to dramatically improve the training speed and performance of our agent. Our architecture is also modularized to allow different models to be independently trained for different phases of the game. We show that the proposed architecture substantially outperforms built-in AI agents of the game as well as average humans in deathmatch scenarios.  Back
 
Keywords:
AI for Gaming, AI and DL Research, GTC Silicon Valley 2018 - ID S8467
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Reinforcement Learning for Multiplayer Agents at SEED
Magnus Nordin (Electronic Arts / SEED)
Over the last couple of years, neural nets have enabled significant breakthroughs in computer vision, voice generation and recognition, translation, and self-driving cars. Neural nets will also be a powerful enabler for future game development. We'l ...Read More
Over the last couple of years, neural nets have enabled significant breakthroughs in computer vision, voice generation and recognition, translation, and self-driving cars. Neural nets will also be a powerful enabler for future game development. We'll give an overview of the potential of neural nets in game development, as well as provide an in-depth look at how we can use neural nets combined with reinforcement learning for new types of game AI.  We will also show some new exciting results from applying deep reinforcement learning to AAA games.  Back
 
Keywords:
AI for Gaming, AI and DL Research, GTC Silicon Valley 2018 - ID S8715
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Optimizing for Real-Time Inference
Donald Brittain (NVIDIA)
Real-time games have an extremely small budget for computations of each frame. Learn the right way to approach real-time performance with inference workloads, taking advantage of the newest technologies available.
Real-time games have an extremely small budget for computations of each frame. Learn the right way to approach real-time performance with inference workloads, taking advantage of the newest technologies available.  Back
 
Keywords:
AI for Gaming, GTC Silicon Valley 2018 - ID S8742
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Deep Learning for Locomotion Animation
Gavriel State (NVIDIA)
We''ll examine tools and technologies that NVIDIA''s GameWorks team is building to leverage the power of deep learning for content creation, demonstrating recent research in ways that neural networks can be used to generate realistic looking human an ...Read More
We''ll examine tools and technologies that NVIDIA''s GameWorks team is building to leverage the power of deep learning for content creation, demonstrating recent research in ways that neural networks can be used to generate realistic looking human animation. We''ll talk about how to apply GPUs for high-performance runtime inferencing of these networks for use in games or real-time VFX scenarios.  Back
 
Keywords:
AI for Gaming, GTC Silicon Valley 2018 - ID S8743
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Production-Level Performance Capture Using Deep Convolutional Neural Networks
Antti Herva (Remedy Games)
We''ll present a machine learning solution that enables cost-efficient creation of large amounts of high-quality facial animation for digital doubles in games. Remedy Entertainment, NVIDIA, and the University of Southern California recently published ...Read More
We''ll present a machine learning solution that enables cost-efficient creation of large amounts of high-quality facial animation for digital doubles in games. Remedy Entertainment, NVIDIA, and the University of Southern California recently published "Production-Level Facial Performance Capture Using Deep Convolutional Neural Networks" as part of the Symposium on Computer Animation. We''ll cover topics including recording a facial animation dataset for an actor, setting up a deep learning project, preprocessing the data, training a deep convolutional neural network, and evaluating the results. We''ll also present a summary of the findings and discuss potential future work.  Back
 
Keywords:
AI for Gaming, Graphics and AI, GTC Silicon Valley 2018 - ID S8734
Streaming:
 
Machine Learning with StarCraft II
Timo Ewalds (DeepMind), Chris Lee (Blizzard)
We''ll present an overview of the StarCraft II machine learning environment, including some basic API examples using C++ and Python. ...Read More

We''ll present an overview of the StarCraft II machine learning environment, including some basic API examples using C++ and Python.

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Keywords:
AI for Gaming, Graphics and AI, GTC Silicon Valley 2018 - ID S8739
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Democratizing Deep Learning with Unity ML-Agents
Arthur Juliani (Unity Technologies)
Unity ML-Agents is an open source AI toolkit that enables machine learning developers and researchers to train agents in realistic, complex scenarios with decreased technical barriers. ML-Agents offers a flexible way to use the Unity Editor and Engin ...Read More
Unity ML-Agents is an open source AI toolkit that enables machine learning developers and researchers to train agents in realistic, complex scenarios with decreased technical barriers. ML-Agents offers a flexible way to use the Unity Editor and Engine to develop and test new AI algorithms quickly and efficiently across games, robotics, and beyond. We''ll walk you through new learning methods that are bundled with the latest version of Unity ML-Agents. This includes, (1) Imitation Learning: Train agents to mimic human behavior. (2) Multi-agent Reinforcement Learning: Train multiple agents together to fulfill cooperative, competitive, and general tasks. We''ll showcase these new learning methods in some interesting training scenarios with real game examples.  Back
 
Keywords:
AI for Gaming, Graphics and AI, GTC Silicon Valley 2018 - ID S8740
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Large-Scale Platform for Multi-Player Online Battle Arena (MOBA) Game AI
Qiang Fu (Tencent AI Lab), Bin Wu (Tencent AI Lab)
We have been developing Multi-Player Online Battle Arena (MOBA) Game AI to push AI research boundaries in pursuit of general AI. One of the key challenges in 5v5 MOBA AI development is how to process massive game replays and feed the data to model tr ...Read More
We have been developing Multi-Player Online Battle Arena (MOBA) Game AI to push AI research boundaries in pursuit of general AI. One of the key challenges in 5v5 MOBA AI development is how to process massive game replays and feed the data to model training in an efficient and reliable manner. To address this, we have built a large-scale game AI platform where millions of CPUs and thousands of GPUs are efficiently scheduled. Powered by our game AI platform and scheduling schemes, our MOBA AI is capable of learning upon billions of high-quality user replay samples per day using both deep learning and self-play.  Back
 
Keywords:
AI for Gaming, Data Center and Cloud Infrastructure, AI and DL Research, GTC Silicon Valley 2018 - ID S8922
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A.I. Disrupting the Future of Content Creation for Games
Eric Risser (Artomatix)
The artistic manpower needed to create a video-game has been increasing exponentially over the years. Thanks to the computational power of NVIDIA GPUs, new AI accelerated workflows are poised to solve this problem, saving artists and studio ...Read More

The artistic manpower needed to create a video-game has been increasing exponentially over the years. Thanks to the computational power of NVIDIA GPUs, new AI accelerated workflows are poised to solve this problem, saving artists and studios time and money, and driving greater creativity. Artomatix is the leading pioneer in this space, its AI-based approach to content creation helps automate many of the mundane, tedious and repetitive tasks artists and designers face every day. This talk introduces the academic theory and history behind Creative AI and then delves into specific use cases and applications such as: Texture Synthesis, Material Enhancement, Hybridization and Style Transfer. Finally, this talk presents the next generation of tools for the creative industries, powered by AI, and gives case studies on how they've been solving some of the game industries largest problems over the past year. Join this session to gain an insight to the future of game creation.

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Keywords:
AI for Gaming, NVIDIA Inception Program, Graphics and AI, GTC Silicon Valley 2018 - ID S8735
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Adaptive Temporal Antialiasing
Adam Marrs (NVIDIA), Rahul Sathe (NVIDIA)
Well discuss Adaptive Temporal Antialiasing (ATAA), a new technique that extends traditional temporal anti-aliasing (TAA) approaches with adaptive ray tracing while conforming to the constraints of a commercial game engine (Unreal Engine 4). Attende ...Read More
Well discuss Adaptive Temporal Antialiasing (ATAA), a new technique that extends traditional temporal anti-aliasing (TAA) approaches with adaptive ray tracing while conforming to the constraints of a commercial game engine (Unreal Engine 4). Attendees will learn how the algorithm removes the blurring and ghosting artifacts common to standard temporal anti-aliasing and achieves image quality approaching 8x supersampling of geometry, shading, and materials while staying within a real-time game frame budget.  Back
 
Keywords:
AI for Gaming, SIGGRAPH 2018 - ID SIG1824
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Real-Time Ray Tracing A Hybrid Ray Raster Roadmap
Chris Wyman, Morgan McGuire (NVIDIA)
Researchers and engineers from NVIDIA joined by leading game studios Epic Games and EA/SEED will present state-of-the-art techniques for ray tracing, sampling, and reconstruction in real time, including recent advances that promise to dramatically ad ...Read More
Researchers and engineers from NVIDIA joined by leading game studios Epic Games and EA/SEED will present state-of-the-art techniques for ray tracing, sampling, and reconstruction in real time, including recent advances that promise to dramatically advance the state of ray tracing in games, simulation, and VR applications.  Back
 
Keywords:
AI for Gaming, SIGGRAPH 2018 - ID SIG1813A
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Ray Tracing Research Update
Morgan McGuire (NVIDIA)
With recent advances in real-time shading techniques, we can now light physically based materials with complex area light sources. Alas, one critical challenge remains: accurate area-light shadowing. The arrival of DXR opens the door to solving this ...Read More
With recent advances in real-time shading techniques, we can now light physically based materials with complex area light sources. Alas, one critical challenge remains: accurate area-light shadowing. The arrival of DXR opens the door to solving this problem via ray tracing, but the correct formulation isn't obvious and there are several potential pitfalls. For instance, the most popular strategy consists of tracing rays to points randomly distributed on the light source and averaging the visibility, but we will show that this is incorrect and produces visual artifacts. Instead, we propose a definition for soft shadows that allows us to compute the correct result, along with an efficient implementation that works with existing analytic area lighting solutions. Note: this talk is an extended presentation of the paper Combining Analytic Direct Illumination and Stochastic Shadows (presented at I3D) that includes additional practical details.   Back
 
Keywords:
AI for Gaming, SIGGRAPH 2018 - ID SIG1813B
Streaming:
 
Real-Time Global Illumination
Jacopo Pantalenoni (NVIDIA)
 
Keywords:
AI for Gaming, SIGGRAPH 2018 - ID SIG1813D
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Low Sample Count Ray Tracing with NVIDIA's Ray Tracing Denoisers
Edward Liu (NVIDIA)
In this session, Edward Liu from NVIDIA will demonstrate how state of the art denoising technologies provided in the Gameworks Ray Tracing module will make 1 sample per pixel ray tracing practical in many scenarios in real-time rendering, including a ...Read More
In this session, Edward Liu from NVIDIA will demonstrate how state of the art denoising technologies provided in the Gameworks Ray Tracing module will make 1 sample per pixel ray tracing practical in many scenarios in real-time rendering, including area light shadows, ambient occlusion, glossy reflections and even indirect diffuse global illumination. Edward will show that one sample per pixel ray tracing with denoising can achieve much improved realism and fidelity when compared with traditional real-time rendering techniques.  Back
 
Keywords:
AI for Gaming, SIGGRAPH 2018 - ID SIG1813E
Streaming:
 
Epic Car Demo
François Antoine (EPIC)
 
Keywords:
AI for Gaming, SIGGRAPH 2018 - ID Sig1813F
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PICA PICA & NVIDIA Turing
Colin Barré-Brisebois (SEED)
 
Keywords:
AI for Gaming, SIGGRAPH 2018 - ID SIG1813G
Streaming:
 
Ray Tracing with Low Sample Counts with NVIDIA's Ray Tracing Denoisers
Edward Liu (NVIDIA)
In this session, Edward Liu from NVIDIA will demonstrate how state of the art denoising technologies provided in the Gameworks Ray Tracing module will make 1 sample per pixel ray tracing practical in many scenarios in real-time renderi ...Read More
In this session, Edward Liu from NVIDIA will demonstrate how state of the art denoising technologies provided in the Gameworks Ray Tracing module will make 1 sample per pixel ray tracing practical in many scenarios in real-time rendering, including area light shadows, ambient occlusion, glossy reflections and even indirect diffuse global illumination. Edward will show that one sample per pixel ray tracing with denoising can achieve much improved realism and fidelity when compared with traditional real-time rendering techniques. Practical tips and guidance for integrating Gameworks Ray Tracing into existing game engines will also be covered.
 

 

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Keywords:
AI for Gaming, SIGGRAPH 2018 - ID SIG1847
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AI for In-Vehicle Applications
Presentation
Media
Fusing Vision and 3D Sensors with AI to Build Cognition Systems
Ronny Cohen (VayaVision), Ido Goren (VayaVision)
Learn how to use GPUs to run 3D and camera deep learning fusion applications for autonomous driving. Cameras provide high resolution 2D information, while lidar has relatively low resolution but provides 3D data. Smart fusing of both RGB and 3D ...Read More

Learn how to use GPUs to run 3D and camera deep learning fusion applications for autonomous driving. Cameras provide high resolution 2D information, while lidar has relatively low resolution but provides 3D data. Smart fusing of both RGB and 3D information, in combination with AI software, enables the building of ultra-high reliability classifiers. This facilitates the required cognition application for semi-autonomous and fully autonomous driving.  

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Keywords:
AI for In-Vehicle Applications, Self-Driving Cars, Deep Learning and AI, GTC Silicon Valley 2017 - ID S7235
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Automated Truck Driving and Platooning with DRIVE PX 2
Devid Will (fka Forschungsgesellschaft Kraftfahrwesen mbH Aachen)
We'll present achievements in the field of automated truck driving, specifically the use case of lane keeping in platooning scenarios based on mirror cameras. Lane detection, generating control parameters, controller, and arbitration functio ...Read More

We'll present achievements in the field of automated truck driving, specifically the use case of lane keeping in platooning scenarios based on mirror cameras. Lane detection, generating control parameters, controller, and arbitration functions all run on the NVIDIA DRIVE PX 2 with three cameras attached to it. 

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Keywords:
AI for In-Vehicle Applications, Self-Driving Cars, Deep Learning and AI, GTC Silicon Valley 2017 - ID S7426
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Building Emotionally Aware Cars
Abdelrahman Mahmoud (Affectiva)
Advanced and autonomous AI systems surround us daily, but as smart as these are, they lack the ability to sense and adapt to human emotions. At Affectiva, our mission is to humanize technology by bringing artificial emotional intelligence (Emotion AI ...Read More
Advanced and autonomous AI systems surround us daily, but as smart as these are, they lack the ability to sense and adapt to human emotions. At Affectiva, our mission is to humanize technology by bringing artificial emotional intelligence (Emotion AI) to the digital world. Using computer vision and deep learning, Affectiva measures facial expressions of emotions. We'll explore the applications of Emotion AI in automotive. We'll show how driver's emotion can be measured in human-driven cars and (semi-) autonomous vehicles to improve road safety and deliver a more personalized transportation experience. In addition, we'll share our findings from over 28 hours of in-car data collected, such as the most frequently observed emotions.  Back
 
Keywords:
AI for In-Vehicle Applications, Deep Learning and AI, Video and Image Processing, GTC Silicon Valley 2017 - ID S7670
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Airbus Vahana - Development of a Self-Piloted Air Taxi
Arne Stoschek (Airbus A3)
Vahana started in early 2016 as one of the first projects at A? the advanced projects outpost of Airbus Group in Silicon Valley. The aircraft we're building doesn't need a runway, is self-piloted, and can automatically detect and avoid o ...Read More

Vahana started in early 2016 as one of the first projects at A? the advanced projects outpost of Airbus Group in Silicon Valley. The aircraft we're building doesn't need a runway, is self-piloted, and can automatically detect and avoid obstacles and other aircraft. Designed to carry a single passenger or cargo, Vahana is meant to be the first certified passenger aircraft without a pilot. We'll discuss the key challenges to develop the autonomous systems of a self-piloted air taxi that can be operated in urban environments.

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Keywords:
AI for In-Vehicle Applications, Deep Learning and AI, GTC Silicon Valley 2017 - ID S7805
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Optimus Ride: Fully Autonomous System for Electric Vehicle Fleets
Sertac Karaman (Optimus Ride Inc.)
Self-driving vehicles will transform the transportation industry, yet must overcome challenges that that go far beyond just technology. We'll discuss both the challenges and opportunities of autonomous mobility and highlight the recent work ...Read More

Self-driving vehicles will transform the transportation industry, yet must overcome challenges that that go far beyond just technology. We'll discuss both the challenges and opportunities of autonomous mobility and highlight the recent work on autonomous vehicle systems by Optimus Ride Inc., an MIT spinoff company based in Boston. The company develops self-driving technologies and is designing a fully autonomous system for electric vehicle fleets.

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Keywords:
AI for In-Vehicle Applications, AI Startup, Self-Driving Cars, GTC Silicon Valley 2017 - ID S7807
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Visual Perception for Autonomous Driving on NVIDIA DRIVE PX 2
Antonio MiguelEspinosa (Universitat Autonoma de Barcelona)
We'll show how to program energy-efficient automotive-oriented NVIDIA GPUs to run computationally intensive camera-based perception algorithms in real time. The Stixel World is a medium-level, compact representation of road scenes that abstr ...Read More

We'll show how to program energy-efficient automotive-oriented NVIDIA GPUs to run computationally intensive camera-based perception algorithms in real time. The Stixel World is a medium-level, compact representation of road scenes that abstracts millions of disparity pixels into hundreds or thousands of stixels. We'll present a fully GPU-accelerated implementation of stixel estimation that produces reliable results at real time (26 frames per second) on the Drive PX 2 platform.   

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Keywords:
AI for In-Vehicle Applications, Self-Driving Cars, Computer Vision and Machine Vision, GTC Silicon Valley 2017 - ID S7848
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The Path to End to End AI Solution (Presented by Inspur)
LeiJun Hu (Inspur)
More and more traditional industries begin to use AI, facing the computing platform, system management, model optimization and other challenges. In this session we build a GPU-based AI end-to-end solution based on a comparative analysis of Caffe ...Read More

More and more traditional industries begin to use AI, facing the computing platform, system management, model optimization and other challenges. In this session we build a GPU-based AI end-to-end solution based on a comparative analysis of Caffe and TensorFlow's computing and communication. 

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Keywords:
AI for In-Vehicle Applications, GTC Silicon Valley 2017 - ID S7854
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Tronis meets Truck: How virtual reality leverages real prototyping of autonomous vehicles
Karl KUFIETA (TWT SCIENCE AND INNOVATION), Michael DITZE (TWT SCIENCE AND INNOVATION)
This talk will describe the process of developing autonomous driving directly from the virtual environment TRONIS, a high resolution virtual environment for prototyping and safeguarding highly automated and autonomous driving functions exploitin ...Read More

This talk will describe the process of developing autonomous driving directly from the virtual environment TRONIS, a high resolution virtual environment for prototyping and safeguarding highly automated and autonomous driving functions exploiting a state of the art gaming engine as introduced by UNREAL. Well showcase this process on a real RC-model with High-End NVIDIA hardware targeting self-driving capabilities on a real world Truck. With the help of TRONIS we make early decisions on sensor configurations e.g. camera, sensor positions and deployed algorithms. The development team works on independent instances of the virtual car which build the foundation for multiple experimental setups.

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Keywords:
AI for In-Vehicle Applications, Self-Driving Cars, Video and Image Processing, GTC Europe 2017 - ID 23318
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Deep learning in MATLAB: From concept to embedded code
Girish Venkataramani (MATHWORKS), Bill CHOU (MATHWORKS), Alexander Schreiber (MATHWORKS)
Learn how to adopt a MATLAB-centric workflow to design, develop, and deploy computer vision and deep learning applications on to GPUs whether on your desktop, a cluster, or on embedded Tegra platforms. The workflow starts with algorithm design i ...Read More

Learn how to adopt a MATLAB-centric workflow to design, develop, and deploy computer vision and deep learning applications on to GPUs whether on your desktop, a cluster, or on embedded Tegra platforms. The workflow starts with algorithm design in MATLAB. The deep learning network is defined in MATLAB and is trained using MATLAB's GPU and parallel computing support. Then, the trained network is augmented with traditional computer vision techniques and the application can be verified in MATLAB. Finally, a compiler auto-generates portable and optimized CUDA code from the MATLAB algorithm, which can be cross-compiled to Tegra. Performance benchmark for Alexnet inference shows that the auto-generated CUDA code is ~2.5x faster than mxNet, ~5x faster than Caffe2 and is ~7x faster than TensorFlow.

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Keywords:
AI for In-Vehicle Applications, Programming Languages, Computer Vision and Machine Vision, GTC Europe 2017 - ID 23321
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Designing GPU-accelerated applications with real-time multisensor frameworks
Xavier ROUAH (INTEMPORA)
As embedded software in intelligent vehicles becomes more and more complex, it becomes critical to automakers and suppliers to use advanced and efficient software solutions. Learn how to dramatically reduces development cycles and how to simplify the ...Read More
As embedded software in intelligent vehicles becomes more and more complex, it becomes critical to automakers and suppliers to use advanced and efficient software solutions. Learn how to dramatically reduces development cycles and how to simplify the deployment of critical real-time applications on embedded targets. In this presentation we will show how RTMaps embedded facilitates porting design from early prototyping stages on PCs down to the most recent ECUs designed for production. RTMaps is a component based software which facilitates the design and the execution of ADAS and HAD applications. It offers an easy-to use drag-and-drop approach for GPU-based computer-vision and AI systems, including an integration of the NVIDIA DriveWorks software modules as independent building-block.  Back
 
Keywords:
AI for In-Vehicle Applications, Self-Driving Cars, HPC and AI, GTC Europe 2017 - ID 23106
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Extensible and Verifiable Nets for Autonomous Driving
Gijs Dubbelman (EINDHOVEN UNIVERSITY OF TECHNOLOGY)
A key technology challenge in computer vision for Autonomous Driving is semantic segmentation of images in a video stream, for which fully-convolutional neural networks (FCNN) are the state-of-the-art. In this research, we explore the functional ...Read More

A key technology challenge in computer vision for Autonomous Driving is semantic segmentation of images in a video stream, for which fully-convolutional neural networks (FCNN) are the state-of-the-art. In this research, we explore the functional and non-functional performance of using a hierarchical classifier head for the FCNN versus using a single flat classifier head. Our experiments are conducted and evaluated on the Cityscapes dataset. On basis of the results, we argue that using a hierarchical classifier head for the FCNN can have specific advantages for autonomous driving. Furthermore, we show real-time usage of our network on the DRIVE PX 2 platform.

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Keywords:
AI for In-Vehicle Applications, GTC Europe 2017 - ID 23215
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Augmented Reality for Navigation and Informational ADAS
Sergii Bykov (APOSTERA GMBH)
Learn how combining machine learning and computer vision with GPU computing helps to create a next-generation informational ADAS experience. This talk will present a real-time software solution that encompasses a set of advanced algorithms to cr ...Read More

Learn how combining machine learning and computer vision with GPU computing helps to create a next-generation informational ADAS experience. This talk will present a real-time software solution that encompasses a set of advanced algorithms to create an augmented reality for the driver, utilizing vehicle sensors, map data, telematics, and navigation guidance. The broad range of features includes augmented navigation, visualization for cases of advanced parking assistance, adaptive cruise control and lane keeping, driver infographics, driver health monitoring, support in low visibility. Our approach augments driver's visual reality with supplementary objects in real time, and works with various output devices such as head unit displays, digital clusters, and head-up displays.

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Keywords:
AI for In-Vehicle Applications, Self-Driving Cars, Computer Vision and Machine Vision, GTC Europe 2017 - ID 23270
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Using Virtualization to Accelerate the Development of ADAS and Automated Driving Functions
Dominik DöRR (IPG AUTOMOTIVE GMBH)
The growing range of functions of ADAS and automated systems in vehicles as well as the progressive change towards agile development processes require efficient test. Testing and validation within simulation are indispensable for this as real pr ...Read More

The growing range of functions of ADAS and automated systems in vehicles as well as the progressive change towards agile development processes require efficient test. Testing and validation within simulation are indispensable for this as real prototypes are not available at all times and the test catalog can be driven repeatedly and reproducibly. This paper presents different approaches to be used in simulation in order to increase the efficiency of development and testing for different areas of application. This comprises the use of virtual prototypes, the utilization of sensor models and the reuse of test scenarios throughout the entire development process, which may also be applied to train artificial intelligence.

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Keywords:
AI for In-Vehicle Applications, Intelligent Machines and IoT, Self-Driving Cars, GTC Europe 2017 - ID 23293
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Adrenaline Fueled Development: Racing with Autonomous Vehicles
Jendrik Jördening (AKKA GERMANY), Anthony NAVARRO (UDACITY)
This talk details a team of 17 Udacity Self-Driving Car students as they attempted to apply deep learning algorithms to win an autonomous vehicle race. At the 2017 Self Racing Cars event held at Thunderhill Raceway in California, the team receiv ...Read More

This talk details a team of 17 Udacity Self-Driving Car students as they attempted to apply deep learning algorithms to win an autonomous vehicle race. At the 2017 Self Racing Cars event held at Thunderhill Raceway in California, the team received a car and had two days before the start of the event to work on the car. In this time, we developed a neural network using Keras and Tensorflow which steered the car based on the input from just one front-facing camera in order to navigate all turns on the racetrack. We will discuss the events leading up to the race, development methods used, and future plans including the use of ROS and semantic segmentation.

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Keywords:
AI for In-Vehicle Applications, Self-Driving Cars, Video and Image Processing, GTC Europe 2017 - ID 23317
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ADAS Development using Advanced Real-Time All-in-the-Loop Simulators
Roberto DE VECCHI (VI-GRADE), Enrico BUSTO (ADDFOR S.P.A.), Guido BAIRATI (VI-GRADE)
This presentation shows how driving simulators together with DNN algorithms can be used in order to streamline and facilitate the development of ADAS and Autonomous Vehicle systems. Driving Simulators provide an excellent tool to develop, test a ...Read More

This presentation shows how driving simulators together with DNN algorithms can be used in order to streamline and facilitate the development of ADAS and Autonomous Vehicle systems. Driving Simulators provide an excellent tool to develop, test and validate control systems for automotive industry. Testing ADAS systems on the driving simulator makes it safer, more affordable and repeateble. This session will focus on a special application in which NVIDIA DRIVE PX 2 has been interfaced with a camera and put in the loop on a driving simulator. Object recognition algorithms have been developed in order to develop and test a Lane Keeping Assist (LKA) system. The robustness of the system can be tested on the simulator by altering the environmental conditions and vehicle parameters.

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Keywords:
AI for In-Vehicle Applications, Self-Driving Cars, Computer Aided Engineering, GTC Europe 2017 - ID 23085
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The learning and intelligent vehicle - a human centric approach to save more lives
Ola Boström (AUTOLIV)
Thanks to recent breakthroughs in AI vehicles will learn and collaborate with humans. There will be a steering wheel in the majority of vehicles for a long time. Therefore a human centric approach is needed in order to save more lives in the tra ...Read More

Thanks to recent breakthroughs in AI vehicles will learn and collaborate with humans. There will be a steering wheel in the majority of vehicles for a long time. Therefore a human centric approach is needed in order to save more lives in the traffic, that is a safe combination of AI and UI.

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Keywords:
AI for In-Vehicle Applications, Self-Driving Cars, GTC Europe 2017 - ID 23124
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Accelerating the TomTom HD Map flywheel
Willem STRIJBOSCH (TOMTOM)
TomTom is leading in HD Maps in coverage and number of OEMs working with our HD Map. Our multi-source, multi-sensor approach leads to HD maps that have greater coverage, are more richly attributed, and have higher quality than single-source, sin ...Read More

TomTom is leading in HD Maps in coverage and number of OEMs working with our HD Map. Our multi-source, multi-sensor approach leads to HD maps that have greater coverage, are more richly attributed, and have higher quality than single-source, single-sensor maps. Hear how were weaving in more and more sources, such as AI-intensive video processing, into our map making to accelerate towards our goal of real-time and highly precise maps for safer and more comfortable driving.

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Keywords:
AI for In-Vehicle Applications, HD Mapping, Self-Driving Cars, GTC Europe 2017 - ID 23130
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Application of Artificial Intelligence at Continental
Umut Ikibas (CONTINENTAL AUTOMOTIVE GMBH), Stefan VOGET (CONTINENTAL AUTOMOTIVE GMBH)
In our presentation we will focus on the automotive electronic control units while giving a rough overview on the others. During the presentation we will show the development process of one of our neural networks using the NVIDIA toolchain and hardwa ...Read More
In our presentation we will focus on the automotive electronic control units while giving a rough overview on the others. During the presentation we will show the development process of one of our neural networks using the NVIDIA toolchain and hardware for training and deployment. Using this example we want to highlight the necessary actions in standardization of e.g. labels, data-interaction and interfaces we need to face in the near future.  Back
 
Keywords:
AI for In-Vehicle Applications, Embedded & Robotics, Computer Vision and Machine Vision, GTC Europe 2017 - ID 23156
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Visual Perception for Autonomous Driving on the NVIDIA DrivePX2 and using SYNTHIA
Juan Carlos Moure (UNIVERSITY AUTONOMA OF BARCELONA), Antonio ESPINOSA (UNIVERSITY AUTONOMA OF BARCELONA)
We present our experience of running computationally intensive camera-based perception algorithms on NVIDIA GPUs. Geometric (depth) and semantic (classification) information is fused in the form of semantic stixels, which provide a rich and comp ...Read More

We present our experience of running computationally intensive camera-based perception algorithms on NVIDIA GPUs. Geometric (depth) and semantic (classification) information is fused in the form of semantic stixels, which provide a rich and compact representation of the traffic scene. We present some strategies to reduce the computational complexity of the algorithms. Using synthetic data generated by the SYNTHIA tool, including slanted roads from a simulation of San Francisco city, we evaluate performance latencies and frame rates on a DrivePX2-based platform.

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Keywords:
AI for In-Vehicle Applications, Computer Vision and Machine Vision, HPC and AI, GTC Europe 2017 - ID 23196
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Deep Learning for Human-centered Video Analysis Solutions
Christian THURAU (TWENTY BILLION NEURONS)
Learn how deep learning is used to process video streams to analyse human behaviour in real-time. We will detail our solution to recognise fine-grained movement patterns of people how they perform everyday actions like e.g. walking, eating, shak ...Read More

Learn how deep learning is used to process video streams to analyse human behaviour in real-time. We will detail our solution to recognise fine-grained movement patterns of people how they perform everyday actions like e.g. walking, eating, shaking hands, talking to each other. The novelty of our technical solution is that our system learns these capabilities from watching lots of video snippets showing such actions. This is exciting because very different applications can be realised with the same algorithms as we follow a purely data-driven, machine learning approach. We will explain what new types of deep neural networks we created and how we employ our Crowd Acting (tm) platform to cost-efficiently acquire hundred thousands videos for that.

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Keywords:
AI for In-Vehicle Applications, Intelligent Video Analytics and Smart Cities, Video and Image Processing, GTC Europe 2017 - ID 23233
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AI for driving monitoring in the future
Martin KRANTZ (SMART EYE)
2017 is the year when the first driver monitoring systems goes into series production with global automotive OEMs. It will be a mainstay as a vital part in most level 3 automated cars but it also has unique stand alone applications such as drows ...Read More

2017 is the year when the first driver monitoring systems goes into series production with global automotive OEMs. It will be a mainstay as a vital part in most level 3 automated cars but it also has unique stand alone applications such as drowsiness and attention, functions that adress approximately half of all traffic accidents. Starting in 2019 there will be more advanced systems going to the market based on improvements in hardware such as high resolution cameras and GPU. Around 2022 there is a third generation of in-car AI to be expected as the hardware will consist of multiple HD cameras running on the latest GPUs.

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Keywords:
AI for In-Vehicle Applications, Self-Driving Cars, Computer Vision and Machine Vision, GTC Europe 2017 - ID 23347
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Learned driving
Urs MULLER (NVIDIA)
In our NVIDIA lab in New Jersey we taught a deep convolutional neural network (DNN) to drive a car by observing human drivers and emulating their behavior. We found that these networks can learn more aspects of the driving task than is commonly ...Read More

In our NVIDIA lab in New Jersey we taught a deep convolutional neural network (DNN) to drive a car by observing human drivers and emulating their behavior. We found that these networks can learn more aspects of the driving task than is commonly learned today. We present examples of learned lane keeping, lane changes, and turns. We also introduce tools to visualize the internal information processing of the neural network and discuss the results.

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Keywords:
AI for In-Vehicle Applications, Embedded & Robotics, GTC Europe 2017 - ID 23385
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Deploying Embedded Computer Vision systems on Military Ground Vehicles
Ross NEWMAN (ABACO)
GPUs can significantly enhance the capabilities of Military Ground Vehicles. In this session we will discuss the challenges facing the integrator of real time vision systems in the Military applications. From video streaming and military streami ...Read More

GPUs can significantly enhance the capabilities of Military Ground Vehicles. In this session we will discuss the challenges facing the integrator of real time vision systems in the Military applications. From video streaming and military streaming protocols through to deploying vision systems for 360 degree situational awareness with AI capabilities. GPUs are being used for enhanced autonomy and in the defence sector and across the board from Ground Vehicles through to Naval and Air applications. Each application space presenting its own challenges through to deployment. Come and find out how the defence industry is addressing these challenges and where the future potential of GPU enabled platforms lie.

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Keywords:
AI for In-Vehicle Applications, Embedded & Robotics, Video and Image Processing, GTC Europe 2017 - ID 23390
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Designing for Utopia
Lewis Horne (UNITI SWEDEN AB)
The autonomous electric car revolution is here and a bright clean future awaits. Yet as we shift to this fundamentally different technology, it becomes clear that perhaps the entire vehicle deserves a rethink. This means not just adding powerful ...Read More

The autonomous electric car revolution is here and a bright clean future awaits. Yet as we shift to this fundamentally different technology, it becomes clear that perhaps the entire vehicle deserves a rethink. This means not just adding powerful computers to outdated vehicle platforms, but instead redesigning the agile device, for this very different future. This process doesnt start with the mechanical structure of yesteryear, instead it starts with the GPU.

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Keywords:
AI for In-Vehicle Applications, GTC Europe 2017 - ID 23439
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Deep Learning for Autonomous Driving
Sepp HOCHREITER (JOHANNES KEPLER UNIVERSITY LINZ), Markus HOFMARCHER (INSTITUTE OF BIOINFORMATICS AT THE JOHANNES KEPLER UNIVERSITY LINZ)
Deep Learning has emerged as the most successful field of machine learning with overwhelming success in industrial speech, language and vision benchmarks. Consequently it evolved into the central field of research for IT giants like Google, face ...Read More

Deep Learning has emerged as the most successful field of machine learning with overwhelming success in industrial speech, language and vision benchmarks. Consequently it evolved into the central field of research for IT giants like Google, facebook, Microsoft, Baidu, and Amazon. Deep Learning is founded on novel neural network techniques, the recent availability of very fast computers, and massive data sets. In its core, Deep Learning discovers multiple levels of abstract representations of the input. Currently the development of self-driving cars is one of the major technological challenges across automotive companies. We apply Deep Learning to improve real-time video data analysis for autonomous vehicles, in particular, semantic segmentation.

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Keywords:
AI for In-Vehicle Applications, GTC Europe 2017 - ID 23472
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AI for Smart Cities
Presentation
Media
Disrupting Vehicle Inspection using Deep Learning
Ilya Bogomolny (UVeye)
We will describe a fast and accurate AI-based GPU accelerated Vehicle inspection system which scans the underside of moving vehicles to identify threatening objects or unlawful substances (bombs, unexposed weapons and drugs), vehicle leaks, wear ...Read More

We will describe a fast and accurate AI-based GPU accelerated Vehicle inspection system which scans the underside of moving vehicles to identify threatening objects or unlawful substances (bombs, unexposed weapons and drugs), vehicle leaks, wear and tear, and any damages that would previously go unnoticed.

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Keywords:
AI for Smart Cities, Intelligent Video Analytics and Smart Cities, GTC Israel 2017 - ID SIL7133
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NVIDIA Metropolis: The Foundation of AI-Enabled Cities
Deepu Talla (NVIDIA)
Smart and safe cities need AI. There are approximately 500 million cameras deployed globally today. When it comes to analyzing that data, traditional methods of video analytics often fall short. AI and deep learning can provide the level of accuracy ...Read More
Smart and safe cities need AI. There are approximately 500 million cameras deployed globally today. When it comes to analyzing that data, traditional methods of video analytics often fall short. AI and deep learning can provide the level of accuracy needed to extract meaningful real-time insights. The result is improved public safety and more efficient city operations. NVIDIA Metropolis is the companys edge-to-cloud platform for the AI City. It includes solutions for deep learning at the edge, in on-prem servers and in the cloud, as well as a comprehensive SDK. During this talk, well provide an overview on NVIDIA Metropolis, its different applications, and its critical role in the creation and expansion of smart and safe cities.  Back
 
Keywords:
AI for Smart Cities, Intelligent Video Analytics and Smart Cities, GTC Israel 2017 - ID SIL7132
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Building the Foundation of America's AI-Enabled Cities
Adam Scraba (NVIDIA)
Why does building AI Cities matter now for America? Why should the U.S. industry and government aggressively develop and deploy AI and deep learning to solve important problems around public safety and operational efficiency in our urban centers? Wha ...Read More
Why does building AI Cities matter now for America? Why should the U.S. industry and government aggressively develop and deploy AI and deep learning to solve important problems around public safety and operational efficiency in our urban centers? What are the global trends that make this the right time to drive these changes? We'll cover these topics and more.  Back
 
Keywords:
AI for Smart Cities, GTC Washington D.C. 2017 - ID DC7122
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Deep Learning for Real-time Threat Detection - From Active Shooters to Armed Robbery
Sean Huver (Deep Science)
We'll explore how deep learning techniques can be used to transform passive surveillance systems into active threat-detection platforms for environments that range from retail, cities, and campuses. Deep Science is deploying deep learning solutions ...Read More
We'll explore how deep learning techniques can be used to transform passive surveillance systems into active threat-detection platforms for environments that range from retail, cities, and campuses. Deep Science is deploying deep learning solutions to spot robberies and assaults as they're occurring in real time.  Back
 
Keywords:
AI for Smart Cities, Cyber Security, GTC Washington D.C. 2017 - ID DC7123
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Cross-Domain Face Recognition Solution Based On GPU-Powered Deep Learning and Inference
Alexander Khanin (VisionLabs)
Government agencies and commercial companies demonstrate high demand for versatile, stable, and highly efficient person identification solutions supporting cross-domain face recognition and person database clusterization in both controlled and uncont ...Read More
Government agencies and commercial companies demonstrate high demand for versatile, stable, and highly efficient person identification solutions supporting cross-domain face recognition and person database clusterization in both controlled and uncontrolled scenarios. Now it's possible to resolve cross-domain face recognition challenges using deep learning and even tasks of quadratic complexity using GPU-powered inference of CNN-based face recognition algorithms. We'll focus on (1) the concept of the GPU-powered platform for cross-domain face recognition; (2) its essential performance and critical technical characteristics; (3) an approach to reaching the demanded efficiency and quality by using the NVIDIA GPU; and (4) providing examples of completed and ongoing projects that demonstrate achieved high-performance and quality parameters in real-life conditions.  Back
 
Keywords:
AI for Smart Cities, Intelligent Video Analytics and Smart Cities, Deep Learning and AI, Cyber Security, Leadership in AI, GTC Washington D.C. 2017 - ID DC7127
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Current State of Autonomous Video Security at Enterprise Scale
Shawn Guan (Umbo CV Inc.)
We'll explore how UMBO CV is leveraging deep learning techniques and GPUs to scale up how video is analyzed in real time and at enterprise scale.
We'll explore how UMBO CV is leveraging deep learning techniques and GPUs to scale up how video is analyzed in real time and at enterprise scale.  Back
 
Keywords:
AI for Smart Cities, Cyber Security, Intelligent Machines and IoT, Deep Learning and AI, GTC Washington D.C. 2017 - ID DC7128
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Leveraging Deep Learning and Video Analysis in Law Enforcement
Tamas Waisz (Axon Research Group), Sanchit Arora (Axon Research Group)
Body-worn cameras have proven to strengthen trust and accountability between law enforcement agencies and the communities they serve. However, large-scale use of body-worn cameras has generated massive amounts of data, which is practically impossible ...Read More
Body-worn cameras have proven to strengthen trust and accountability between law enforcement agencies and the communities they serve. However, large-scale use of body-worn cameras has generated massive amounts of data, which is practically impossible for these agencies to use effectively. This has led to significant, and unproductive, time spent manually analyzing data. Axon Research is using the latest advances in deep learning and GPU acceleration to enable increased efficiency across the body-worn camera continuum by accelerating the many manual, time-consuming workflows in public safety, such as redacting footage in response to a public request. Attendees will hear the potential impact of large-scale deep learning on law enforcement and public safety information management.  Back
 
Keywords:
AI for Smart Cities, Cyber Security, GTC Washington D.C. 2017 - ID DC7142
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New Video Search Capabilities for Public Safety through Intelligent Video Analytics and Deep Learning
Mahesh Sapharishi (Avigilon), Moussa Doumbouya (Avigilon)
For security teams working to ensure public safety, the ability to minimize incident response time and speed forensic investigations is critical. We'll discuss a new end-to-end, deep learning, and GPU-reliant architecture and video search engine for ...Read More
For security teams working to ensure public safety, the ability to minimize incident response time and speed forensic investigations is critical. We'll discuss a new end-to-end, deep learning, and GPU-reliant architecture and video search engine for video data being deployed to solve this.  Back
 
Keywords:
AI for Smart Cities, Cyber Security, GTC Washington D.C. 2017 - ID DC7238
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How Deep Learning Reverses Resolution-Degrading Effects of Conventional Video Capture
Nathan Wheeler (Entropix, Inc.), Michael Korkin (Entropix, Inc.), Dwight Linden (Entropix, Inc.)
We'll showcase both the technology and use-cases for applying convolutional neural networks and GPUs to reverse the resolution-degrading effects of optical blur and sensor sampling, in order to reconstruct color video to nine times its captured pixe ...Read More
We'll showcase both the technology and use-cases for applying convolutional neural networks and GPUs to reverse the resolution-degrading effects of optical blur and sensor sampling, in order to reconstruct color video to nine times its captured pixel density.  Back
 
Keywords:
AI for Smart Cities, Intelligent Video Analytics and Smart Cities, GTC Washington D.C. 2017 - ID DC7250
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The NVIDIA AI City Challenge - AI for Smarter Traffic and Safer Streets
Milind Naphade (NVIDIA), Honghui Shi (University of Illinois at Urbana-Champaign), Zheng Tang (University of Washington)
We'll introduce the of the AI cities challenge winners announced at GTC2017. Honghui Shi from University of Illinois at Urbana-Champaign who will do a ten minute presentation on multiple-Kernel based vehicle tracking Using 3D deformable models. Zhe ...Read More
We'll introduce the of the AI cities challenge winners announced at GTC2017. Honghui Shi from University of Illinois at Urbana-Champaign who will do a ten minute presentation on multiple-Kernel based vehicle tracking Using 3D deformable models. Zheng Tang will then present on effective object detection from traffic camera videos.  Back
 
Keywords:
AI for Smart Cities, GTC Washington D.C. 2017 - ID DC7252
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AI in Healthcare
Presentation
Media
Using Deep Learning Algorithms to Achieve more Efficient and Enhanced-Value Radiology Reporting
Hayit Greenspan (Tel-Aviv University)
We'll introduce the RADLogics Virtual Resident, which uses machine learning image analysis to process the enormous amount of imaging data associated with CTs, MRIs and X-rays, and introduces within minutes, a draft reportwith key imagesinto ...Read More

We'll introduce the RADLogics Virtual Resident, which uses machine learning image analysis to process the enormous amount of imaging data associated with CTs, MRIs and X-rays, and introduces within minutes, a draft reportwith key imagesinto the reporting system. We'll present several examples of automated analysis using deep learning tools, in applications of Chest CT and Chest X-ray. We'll show the algorithmic solutions used, and quantitative evaluation of the results, along with actual output into the report. It is our goal to provide many such automated applications, to automatically detect and quantify findings thus enabling efficient and augmented reporting.   

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Keywords:
AI in Healthcare, Healthcare and Life Sciences, Computer Vision and Machine Vision, Medical Imaging and Radiology, GTC Silicon Valley 2017 - ID S7839
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Harnessing AI in Healthcare
Dr.Keith Dreyer (Massachusetts General Hospital and Harvard Professor)
As computers outperform humans at complex cognitive tasks, disruptive innovation will increasingly remap the familiar with waves of creative destruction.  And in healthcare, nowhere is this more apparent or imminent than at the crossroads o ...Read More

As computers outperform humans at complex cognitive tasks, disruptive innovation will increasingly remap the familiar with waves of creative destruction.  And in healthcare, nowhere is this more apparent or imminent than at the crossroads of Radiology and the emerging field of Clinical Data Science. As leaders in our field, we must shepherd the innovations of cognitive computing by defining its role within diagnostic imaging, while first and foremost ensuring the continued safety of our patients.  If we are dismissive, defensive or self-motivated - industry, payers and provider entities will innovate around us achieving different forms of disruption, optimized to serve their own needs.  To maintain our leadership position, as we enter the era of machine learning, it is essential that we serve our patients by directly managing the use of clinical data science towards the improvement of carea position which will only strengthen our relevance in the care process as well as in future federal, commercial and accountable care discussions. We'll explore the state of clinical data science in medical imaging and its potential to improve the quality and relevance of radiology as well as the lives of our patients.

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Keywords:
AI in Healthcare, Healthcare and Life Sciences, Deep Learning and AI, GTC Silicon Valley 2017 - ID S7840
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How Triage is Detecting Skin Cancer from Smarthphones with Deep Learning (Presented by Triage)
Tory Jarmain (Triage)
You'll learn how Triage is using deep learning to diagnose skin cancer from any smartphone. 1 in 3 cancer diagnoses is skin cancer and 1 in 5 Americans will develop skin cancer in their lifetime. The average wait time to see a dermatologist ...Read More

You'll learn how Triage is using deep learning to diagnose skin cancer from any smartphone. 1 in 3 cancer diagnoses is skin cancer and 1 in 5 Americans will develop skin cancer in their lifetime. The average wait time to see a dermatologist in the United States is 1 month and even greater in other parts of the world. In that time skin disorders can worsen or become life threatening. Triage's Co-Founder and CEO, Tory Jarmain, will demonstrate how they trained a Convolutional Neural Network to instantly detect 9 in 10 cancer cases with beyond dermatologist-level accuracy. Tory will also show how Triage's technology can identify skin disorders across 23 different categories including acne, eczema, warts and more using Deep Residual Networks.

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Keywords:
AI in Healthcare, Healthcare and Life Sciences, GTC Silicon Valley 2017 - ID S7842
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AgeAtHome - Deep Learning at the Edge (Presented by IBM)
David CMartin (IBM Watson Cloud CTO Office), Dima Rekesh (Optum Technology)
The need for helping elderly individuals or couples remain in their home is increasing as our global population ages. Cognitive processing offers opportunities to assist the elderly by processing information to identify opportunities for caregiv ...Read More

The need for helping elderly individuals or couples remain in their home is increasing as our global population ages. Cognitive processing offers opportunities to assist the elderly by processing information to identify opportunities for caregivers to offer assistance and support.  This project seeks to demonstrate means to improve the elderlys' ability to age at home through understanding of daily activities inferred from passive sensor analysis. This project is an exploration of the IBM Watson Cloud and Edge docker-based Blue Horizon platforms for the use of high-fidelity, low-latency, private sensing and responding at the edge using a RaspberryPi, including deep learning using NVIDIA DIGITS software, K80 GPU servers in the IBM Cloud, and Jetson TX2 edge computing.

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Keywords:
AI in Healthcare, Deep Learning and AI, GTC Silicon Valley 2017 - ID S7857
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Using Clinical AI for improved medical decision making for both providers and patients
Tashfeen Suleman (CloudMedx Inc)
Majority of the healthcare data stored is stored in healthcare workflows, electronic health records, and consumer devices. This data is largely untouched. CloudMedx has built a clinical framework that uses advanced algorithms and AI to look at t ...Read More

Majority of the healthcare data stored is stored in healthcare workflows, electronic health records, and consumer devices. This data is largely untouched. CloudMedx has built a clinical framework that uses advanced algorithms and AI to look at this data in both structured and unstructured formats using Natural Language Processing and Machine Learning to bring insights such as patient risks, outcomes, and action items to the point of care.  The goal of the company is to save lives and improve clinical workflows. 

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Keywords:
AI in Healthcare, Healthcare and Life Sciences, GTC Silicon Valley 2017 - ID S7863
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Doctors & Developers: Combining Expertise With VR and AI To Improve Medical Training and Simulation
Shauna Heller (Clay Park VR)
Learn how doctors aided in the design process to create authentic VR trauma room scenarios; how expert content and simulation devs crafted a VR experience that would have impact in a world where there's no room for error and why Oculus suppo ...Read More

Learn how doctors aided in the design process to create authentic VR trauma room scenarios; how expert content and simulation devs crafted a VR experience that would have impact in a world where there's no room for error and why Oculus supports the program.  Experiential learning is among the best ways to practice for pediatric emergencies. However, hospitals are spending millions on expensive and inefficient mannequin-based training that does not consistently offer an authentic experience for med students or offer convenient repeatability. Join us for a case study on a groundbreaking pilot program that brought together Children's Hospital Los Angeles with two unique VR and AI dev teams to deliver VR training simulations for the most high stakes emergencies hospitals see: pediatric trauma.

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Keywords:
AI in Healthcare, Virtual Reality and Augmented Reality, GTC Silicon Valley 2017 - ID S7865
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Data-Driven Innovation in Health Policy and Healthcare Practice
Ran Balicer (Clalit Research Institute)
Health systems worldwide need greater availability and intelligent integrated use of data and information technology. Clalit has been leading innovative interventions using clinical data to drive people-centered targeted and effective care model ...Read More

Health systems worldwide need greater availability and intelligent integrated use of data and information technology. Clalit has been leading innovative interventions using clinical data to drive people-centered targeted and effective care models, for chronic disease prevention and control. Clalit actively pursues a paradigm shift to properly deal with these challenges, using IT, data and advanced analytics to transform its healthcare system to one which can bridge the silos of care provision in a patient-centered approach, and move from reactive therapeutic to proactive preventive care. In the presentation we will detail specific examples that allowed for reducing healthcare disparities, preventing avoidable readmissions, and improving control of key chronic diseases.

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Keywords:
AI in Healthcare, GTC Israel 2017 - ID SIL7151
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Deep Learning for Helping in Diagnosis Tasks - A Radiologist Assistant Technology (Presented by IBM)
Michal Rosen-Zvi (IBM Research, Haifa Research Labs, Israel)
This talk is about deriving insights and generating a Deep Learning based technology that is trained on Real World Data - electronic health records and medical images - and provides a support for radiologists and other physicians in their diagnosis p ...Read More
This talk is about deriving insights and generating a Deep Learning based technology that is trained on Real World Data - electronic health records and medical images - and provides a support for radiologists and other physicians in their diagnosis process. Breast cancer diagnosis based on clinical history and medical imaging will serve as a running example while a set of technologies developed at IBM Research Haifa will be reviewed. The talk will also provide information regarding value gained from leveraging the IBM PowerAI platform - the world's fastest AI platform. It combines state of the art hardware with complete deep learning software distribution.  Back
 
Keywords:
AI in Healthcare, GTC Israel 2017 - ID SIL7106
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Identifying Hundreds of Genetic Disorders Using Deep Learning
Yaron Gurovich (FDNA)
In this talk, FDNA will present how deep learning is used to build an applicable framework that is used to aid in identification of hundreds of genetic disorders and help kids all over the world. Genetic Disorders affect one in every ten people. ...Read More

In this talk, FDNA will present how deep learning is used to build an applicable framework that is used to aid in identification of hundreds of genetic disorders and help kids all over the world. Genetic Disorders affect one in every ten people. Many of these diseases are characterized by observable traits of the affected individuals - a 'phenotype'. In many cases, this phenotype is especially noticeable in the facial features of the patients, Down syndrome for example. But most such conditions have subtle facial patterns and are harder to diagnose. FDNA will describe their solution, its ability to generalize well for hundreds of Disorders while learning from a small amount of images per class, and its application for genetic clinicians and researchers.

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Keywords:
AI in Healthcare, GTC Israel 2017 - ID SIL7131
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Disruptive Changes in Ophthalmology by Deep Learning
Aaron Lee (University of Washington)
Hear about how GPU technology is disrupting the way your eye doctor works and how ophthalmic research is performed today. The rise of electronic medical records in medicine has created mountains of big data, particularly in ophthalmology, where many ...Read More
Hear about how GPU technology is disrupting the way your eye doctor works and how ophthalmic research is performed today. The rise of electronic medical records in medicine has created mountains of big data, particularly in ophthalmology, where many discrete quantitive clinical elements like visual acuity can be tied to rich imaging datasets. We'll explore the transformative nature that GPU acceleration has played in accelerating clinical research and show real-life examples of deep learning applications to ophthalmology in creating new steps forward in automated diagnoses, image segmentation, and computer-aided diagnoses.  Back
 
Keywords:
AI in Healthcare, Computer Vision and Machine Vision, GTC Washington D.C. 2017 - ID DC7108
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Intelligent Process Automation with Jetson-TX2
Murali Kaundinya (Merck)
We'll focus on engineering and building an intelligent process automation solution with Angular, Activiti, TensorFlow, and Jetson TX2. While workflow and BPM solutions have long been a commodity capability, the reality is that many business processe ...Read More
We'll focus on engineering and building an intelligent process automation solution with Angular, Activiti, TensorFlow, and Jetson TX2. While workflow and BPM solutions have long been a commodity capability, the reality is that many business processes and their decision making have to be supplemented with sufficient context and strong analytics. Without the context and analytics, some decisions get delayed or never get made thus defeating the purpose of automation. Even the most basic digitization effort attempting to do away with paper and dealing with OCR requires a significant amount of context and analytics to be done correctly. We'll show how to build a lightweight "intelligent process automation" capability by assembling open source components such as Angular, Activiti, and TensorFlow, where the OCR processing runs on the Jetson TX2 system. We'll describe the modular architecture and the actual code in assembling a fully functional product, and we'll share learned lessons.  Back
 
Keywords:
AI in Healthcare, Deep Learning and AI, Computer Vision and Machine Vision, GTC Washington D.C. 2017 - ID DC7131
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Effectiveness of Deep Learning Compared to Machine Learning in Applied HealthCare
Julie Zhu (United Healthcare Group - Optum Technology), Ravishankar Rajagopalan (Optum Technology), Dima Rekesh (United Healthcare Group - Optum Technology)
We'll present a use case of applying machine learning and deep learning to the task of imputing/predicting a medical patient diagnosis based on data elements of their member, medical, and pharmacy claims. We'll introduce deep learning approaches, a ...Read More
We'll present a use case of applying machine learning and deep learning to the task of imputing/predicting a medical patient diagnosis based on data elements of their member, medical, and pharmacy claims. We'll introduce deep learning approaches, a side by side comparison of machine learning models vs. deep learning models, and illustrate the operation and business value of deep learning models.  Back
 
Keywords:
AI in Healthcare, Deep Learning and AI, GTC Washington D.C. 2017 - ID DC7154
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A Machine Learning Approach for Tissue Biomechanics
Anand Santhanam (University of California, Los Angeles.)
We're developing a machine learning strategy for improving lung and liver cancer treatments. Specifically, near-real-time lung and liver tissue elasticity estimations using only deformation maps that would typically be available from 4DCT lung image ...Read More
We're developing a machine learning strategy for improving lung and liver cancer treatments. Specifically, near-real-time lung and liver tissue elasticity estimations using only deformation maps that would typically be available from 4DCT lung images. The key technical innovation presented by our work is the integration of patient-specific, GPU-based biomechanical models with GPU-based machine learning approaches. For learning purposes, we employed TensorFlow. Enabling such measurements within the radiotherapy setup facilitates multiple advancements, including: (a) functional lung and liver preserving radiotherapy treatment planning, where the patient's treatment efficacy and quality of life can be significantly improved, (b) patient treatment response monitoring, where the impact of treatment on the patient can be monitored, and (c) novel therapeutic approaches, where the treatment can be adapted to the patient's disease conditions.  Back
 
Keywords:
AI in Healthcare, VR and Simulation, Medical Imaging and Radiology, GTC Washington D.C. 2017 - ID DC7216
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Extreme Computing, Clinical Medicine and GPUs
Joel Saltz (Stony Brook Medicine and College of Engineering and Applied Sciences Stony Brook)
Images and sensors provide crucial information needed to make treatment decisions and machine learning methods are increasingly employed to supplement subjective human image interpretations and to integrate heterogeneous collections of information. W ...Read More
Images and sensors provide crucial information needed to make treatment decisions and machine learning methods are increasingly employed to supplement subjective human image interpretations and to integrate heterogeneous collections of information. We'll describe the rapidly changing landscape of medical images and sensors from both a computing, data, and medical point of view. We'll then do a deep dive in the area of pathology image analytics along with contributions made by deep learning methods to precision medicine and clinical diagnostics. Finally, we'll address the pivotal role of GPUs in supporting all of these computations and describe the roles of GPU-related tools, languages, and libraries in the medical image and sensor analytics.  Back
 
Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Washington D.C. 2017 - ID DC7248
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Computational Pathology in Practice: From Cluster to Clinic
Thomas Fuchs (Memorial Sloan Kettering Cancer Center)
Pathology is in the midst of a revolution from a qualitative to a quantitative discipline. This transformation is being fueled by machine and deep -learning. At Memorial Sloan Kettering we're building a computational pathology AI using an NVIDIA G ...Read More
Pathology is in the midst of a revolution from a qualitative to a quantitative discipline. This transformation is being fueled by machine and deep -learning. At Memorial Sloan Kettering we're building a computational pathology AI using an NVIDIA GPU cluster and a petabyte of clinical data. The goal is to transition the microscopic histological assessment of tissues from a manual and subjective process to a quantitative one. An AI that enables pathologists to efficiently and with speed, perform a pathological assessment that is more reproducible and objective. An AI that will facilitate large-scale, quantitative screening for correlations between tissue morphology and genetic panels like MSK-IMPACT.  Back
 
Keywords:
AI in Healthcare, GTC Washington D.C. 2017 - ID DC7254
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Activating Tissue Data in the Era of Computational Medicine
Hunter Jackson (Proscia Inc)
Pathology departments and translational research centers have amassed invaluable, untapped information in the form of glass pathology slides. The recent proliferation of digital pathology has shifted pathology into the era of computational medicine b ...Read More
Pathology departments and translational research centers have amassed invaluable, untapped information in the form of glass pathology slides. The recent proliferation of digital pathology has shifted pathology into the era of computational medicine by creating the opportunity to quantify and integrate tissue data to supplement the existing cancer model centered around human expertise, and corresponding patient history and "-omic" data. With the help of clinical partners and massive computing infrastructures provided by NVIDIA, we are developing deep learning powered tools that activate those digital slides to address problems in the clinic and inform disease prognosis and therapeutic plans. This talk will outline our approach to deep learning based pathology image processing, including coarse feature embedding using fully convolutional networks and stain normalization using Generative Adversarial networks. It will also highlight a few of our recent successes in this domain including image classification applications for identifying metastases in breast and gastric lymph nodes with and image biomarker generation using deep learning to predict lymph node metastasis from the primary tumor histology.  Back
 
Keywords:
AI in Healthcare, GTC Washington D.C. 2017 - ID DC7255
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Applying Neural Networks to Enhance Drug Discovery
John Tenney (Berkeley Lights)
Berkeley Lights has developed unique capabilities to select, sort and annotate single live cells using light with a nanofluidic chip.  By combining this capability with deep learning, Convolutional Neural Networks and Nvidia GPUs, BLI can accura ...Read More
Berkeley Lights has developed unique capabilities to select, sort and annotate single live cells using light with a nanofluidic chip.  By combining this capability with deep learning, Convolutional Neural Networks and Nvidia GPUs, BLI can accurately identify, characterize and assay cells.  Applying this to antibody discovery for novel therapeutics, the Beacon platform automatically identifies the rare cells out of thousands that are producing the antibody of interest. This can reduce timelines from months to less than a week allowing scientist to iterate significantly faster.  Back
 
Keywords:
AI in Healthcare, GTC Washington D.C. 2017 - ID DC7258
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Alexa, Tell me how Kaldi and Deep Learning Revolutionized Automatic Speech Recognition
Sanjeev Khudanpur (The Johns Hopkins University)
We'll review the history of automatic speech recognition (ASR) technology, and show how deep neural networks have revolutionized the field within the last 5 years, giving birth to Alexa, enhancing Siri and nudging Google Home to market, and generall ...Read More
We'll review the history of automatic speech recognition (ASR) technology, and show how deep neural networks have revolutionized the field within the last 5 years, giving birth to Alexa, enhancing Siri and nudging Google Home to market, and generally making ASR a household phenomenon. The story will be told from the viewpoint of Kaldi, a widely used set of open-source ASR tools in both academia and industry, touching on some key milestones and seminal developments along this short-yet-exciting journey, such as suitable network architectures, novel training criteria, and scalable optimization algorithms, along with prescient research funding, realistic data sets, and competitive benchmark tests conducted by neutral entities.  Back
 
Keywords:
AI in Healthcare, GTC Washington D.C. 2017 - ID DC7260
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The FELIX Project. Deep Network for the Early Detection of Pancreatic Cancer
Elliot Fishman (Johns Hopkins University), Alan Yuille (Johns Hopkins University)
We'll present our experience in the development of a multidisciplinary project for the early detection of pancreatic cancer. We'll present the FELIX project goals and the challenges including how we defined our goals and collected over 1300 annotat ...Read More
We'll present our experience in the development of a multidisciplinary project for the early detection of pancreatic cancer. We'll present the FELIX project goals and the challenges including how we defined our goals and collected over 1300 annotated CT datasets for developing and testing deep learning algorithms. We will describe the deep network that we are using and discuss our results and future directions. This type of research has great potential for developing assistive devices for Radiologists as a second reader, high quality annotated datasets are critical for making progress in this field and are time consuming and expensive to acquire. This type of research is performed best by teams that combine expertise in Radiology and Machine Learning.  Back
 
Keywords:
AI in Healthcare, GTC Washington D.C. 2017 - ID DC7266
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From Challenges to Impact of Machine Learning in Clinical Practice
Keith Dreyer (Partners HealthCare), Alejandro Frangi (CISTIB / The University of Sheffield), Abdul Hamid Halabi (NVIDIA), Wiro Niessen (Medical Image Computing and Computer Assisted Interventions (MICCAI)), Mike Tilkin (American College of Radiology (ACR))
The increasing availability of large medical imaging data resources with associated clinical data, combined with the advances in the field of machine learning, hold large promises for disease diagnosis, prognosis, therapy planning and therapy mo ...Read More

The increasing availability of large medical imaging data resources with associated clinical data, combined with the advances in the field of machine learning, hold large promises for disease diagnosis, prognosis, therapy planning and therapy monitoring. As a result, the number of researchers and companies active in this field has grown exponentially, resulting in a similar increase in the number of papers and algorithms. A number of issues need to be addressed to increase the clinical impact of the machine learning revolution in radiology. First, it is essential that machine learning algorithms can be seamlessly integrated in the clinical workflow. Second, the algorithm should be sufficiently robust and accurate, especially in view of data heterogeneity in clinical practice. Third, the additional clinical value of the algorithm needs to be evaluated. Fourth, it requires considerable resources to obtain regulatory approval for machine learning based algorithms. In this workshop, the ACR and MICCAI Society will bring together expertise from radiology, medical image computing and machine learning, to start a joint effort to address the issues above.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8897
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Automated Segmentation of Suspicious Breast Masses from Ultrasound Images
Viksit Kumar (Mayo Clinic College of Medicine and Science)
Learn how to apply deep learning for detecting and segmenting suspicious breast masses from ultrasound images. Ultrasound images are challenging to work with due to the lack of standardization of image formation. Learn the appropriate data augme ...Read More

Learn how to apply deep learning for detecting and segmenting suspicious breast masses from ultrasound images. Ultrasound images are challenging to work with due to the lack of standardization of image formation. Learn the appropriate data augmentation techniques, which do not violate the physics of ultrasound imaging. Explore the possibilities of using raw ultrasound data to increase performance. Ultrasound images collected from two different commercial machines are used to train an algorithm to segment suspicious breast with a mean dice coefficient of 0.82. The algorithm is shown to perform at par with conventional seeded algorithm. However, a drastic reduction in computation time is observed enabling real-time segmentation and detection of breast masses.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8525
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Building a GPU-Accelerated Short-Read Aligner for Bisulfite-Treated DNA Sequences
Richard Wilton (Johns Hopkins University)
It is not always easy to accelerate a complex serial algorithm with CUDA parallelization. A case in point is that of aligning bisulfite-treated DNA (bsDNA) sequences to a reference genome. A simple CUDA adaptation of a CPU-based implementation c ...Read More

It is not always easy to accelerate a complex serial algorithm with CUDA parallelization. A case in point is that of aligning bisulfite-treated DNA (bsDNA) sequences to a reference genome. A simple CUDA adaptation of a CPU-based implementation can improve the speed of this particular kind of sequence alignment, but it's possible to achieve order-of-magnitude improvements in throughput by organizing the implementation so as to ensure that the most compute-intensive parts of the algorithm execute on GPU threads.

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Keywords:
AI in Healthcare, Bioinformatics & Genomics, GTC Silicon Valley 2018 - ID S8130
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Ultrasound Medical Imaging in the GPU Era
Marcin Lewandowski (us4us Ltd.)
Fast, inexpensive and safe, ultrasound imaging is the modality of choice for the first level of medical diagnostics. The emerging solutions of portable and hand-held 2/3D scanners, advanced imaging algorithms, and deep learning promise further d ...Read More

Fast, inexpensive and safe, ultrasound imaging is the modality of choice for the first level of medical diagnostics. The emerging solutions of portable and hand-held 2/3D scanners, advanced imaging algorithms, and deep learning promise further democratization of this technology. During the session, we will present an overview of ultrasound imaging techniques in medical diagnostics, explore the future of ultrasound imaging enabled by GPU processing, as well as set out the path to the conception of a portable 3D scanner. We will also demonstrate our hardware developments in ultrasound platforms with GPU-based processing. Having started with one large research scanner, we have begun our migration towards more commercially-viable solutions with a small hand-held unit built on the mobile GPU NVidia Tegra X1.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8421
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Targeted Sequencing for All on S5 an S5 XL: GPUs Make It Happen
Mohit Gupta (Thermo Fisher Scientific)
We'll disscuss how GPUs are playing a central role in making advances in Ion Torrent's targeted sequencing workflow and talk about the S5 DNA sequencer from Ion Torrent that is enabling democratization of sequencing market and accel ...Read More

We'll disscuss how GPUs are playing a central role in making advances in Ion Torrent's targeted sequencing workflow and talk about the S5 DNA sequencer from Ion Torrent that is enabling democratization of sequencing market and accelerating research in precision medicine at a breathtaking pace with the help of GPUs. We'll highlight our work in liquid biopsy and non-invasive prenatal testing and how the breadth in technology offerings in semiconductor chips gives us the scale of sequencing from small panels to exomes. We'll discuss our analysis pipeline and the latest and greatest in algorithm development and acceleration on GPUs as well as our experiences ranging from Fermi to Pascal GPU architectures. 

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Keywords:
AI in Healthcare, Bioinformatics & Genomics, GTC Silicon Valley 2018 - ID S8419
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Computational Pathology at Scale: Changing Clinical Practice One Petabyte at a Time
Thomas Fuchs (Memorial Sloan Kettering Cancer Center)
How can we train medical deep learning models at a petabyte scale and how can these models impact clinical practice? We will discuss possible answers to these questions in the field of Computational Pathology. Pathology is in the midst of a revo ...Read More

How can we train medical deep learning models at a petabyte scale and how can these models impact clinical practice? We will discuss possible answers to these questions in the field of Computational Pathology. Pathology is in the midst of a revolution from a qualitative to a quantitative discipline. This transformation is fundamentally driven by machine learning in general and computer vision and deep learning in particular. With the help of PAIGE.AI we are building a clinical-grade AI at Memorial Sloan Kettering Cancer Center. The models are trained based on petabytes of image and clinical data on top of the largest DGX-1 V100 cluster in pathology. The goal is not only to automated cumbersome and repetitive tasks, but to impact diagnosis and treatment decisions in the clinic. This talk will focus on our recent advances in deep learning for tumor detection and segmentation, on how we train these high capacity models with annotations collected from pathologists, and how the resulting systems are implemented in the clinic.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8960
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Machine Learning in Precision Medicine: Quantitative Medical Imaging, Artificial Intelligence, GPU Efficiency
Milan Sonka (University of Iowa)
Machine Learning in Precision Medicine: Patient-Specific Treatment Enabled by Quantitative Medical Imaging, Artificial Intelligence, and GPU Efficiency The attendees will learn about the need for and use of machine learning in today's patien ...Read More

Machine Learning in Precision Medicine: Patient-Specific Treatment Enabled by Quantitative Medical Imaging, Artificial Intelligence, and GPU Efficiency The attendees will learn about the need for and use of machine learning in today's patient-centered healthcare. The talk will focus on general approaches requiring machine learning to obtain image-based quantitative features, reach patient diagnoses, predict disease outcomes, and identify proper precision-treatment strategies. While the presented methods are general in nature, examples from cardiovascular disease management will be used to demonstrate the need for and power of machine learning enabled by the performance advantages of GPU computation.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8892
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Workflow and Regulatory Challenges to Algorithm Implementation
Mike Tilkin (American College of Radiology (ACR))
AI in medical imaging has the potential to provide radiology with an array of new tools that will significantly improve patient care. To realize this potential, AI algorithm developers must engage with physician experts and navigate domains such ...Read More

AI in medical imaging has the potential to provide radiology with an array of new tools that will significantly improve patient care. To realize this potential, AI algorithm developers must engage with physician experts and navigate domains such as radiology workflow and regulatory compliance. This session will discuss a pathway for clinical implementation, and cover ACR's efforts in areas such as use case development, validation, workflow integration, and monitoring.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8994
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R&D on Medical Imaging
Mei Han (Ping An Technology, US Research Lab)
In this talk I will describe the research and development work on medical imaging, done at PingAn Technology and Google Cloud, covering five different tasks. I'll present the technical details of the deep learning approaches we have develope ...Read More

In this talk I will describe the research and development work on medical imaging, done at PingAn Technology and Google Cloud, covering five different tasks. I'll present the technical details of the deep learning approaches we have developed, and share with the audiences the research direction and scope in the medical fields at PingAn technology and PingAn USA Lab.

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Keywords:
AI in Healthcare, Computer Vision, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8930
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Not Just a Black Box: Interpretable Deep Learning for Genomics and Beyond
Avanti Shrikumar (Stanford University)
Deep learning models give state-of-the-art results on diverse problems, but their lack of interpretability is a major problem. Consider a model trained to predict which DNA mutations cause disease: if the model performs well, it has likely ident ...Read More

Deep learning models give state-of-the-art results on diverse problems, but their lack of interpretability is a major problem. Consider a model trained to predict which DNA mutations cause disease: if the model performs well, it has likely identified patterns that biologists would like to understand. However, this is difficult if the model is a black box. We present algorithms that provide detailed explanations for individual predictions made by a deep learning model and discover recurring patterns across the entire dataset. Our algorithms address significant limitations of existing interpretability methods. We show examples from genomics where the use of deep learning in conjunction with our interpretability algorithms leads to novel biological insights.

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Keywords:
AI in Healthcare, Bioinformatics & Genomics, GTC Silicon Valley 2018 - ID S8907
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Identifying New Therapeutics for Parkinson's Disease Using Virtual Neurons on an Azure Hosted GPU Cluster
Andy Lee (Neuroinitiative)
Learn how to apply recent advances in GPU and open data to unravel the mysteries of biology and etiology of disease. Our team has built data driven simulated neurons using CUDA and open data, and are using this platform to identify new therapeut ...Read More

Learn how to apply recent advances in GPU and open data to unravel the mysteries of biology and etiology of disease. Our team has built data driven simulated neurons using CUDA and open data, and are using this platform to identify new therapeutics for Parkinson's disease with funding from the Michael J. Fox Foundation. In this session I'll discuss the open data which enables our approach, and how we are using Nvidia Tesla cards on Microsoft Azure to dynamically scale to more than 100,000 GPU cores while managing technology costs.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8386
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A Deep Learning-Based Intelligent Reference Library for Diagnostic Decision Support in Lung Cancer Screening
Daniel Golden (Arterys), Sean Sall (Arterys)
Radiological diagnosis and interpretation should not take place in a vacuum -- but today, it does. One of the greatest challenges the radiologist faces when interpreting studies is understanding the individual patient in the context of the milli ...Read More

Radiological diagnosis and interpretation should not take place in a vacuum -- but today, it does. One of the greatest challenges the radiologist faces when interpreting studies is understanding the individual patient in the context of the millions of patients who have come previously. Without access to historical data, radiologists must make clinical decisions based only on their memory of recent cases and literature. Arterys is working to empower the radiologist with an intelligent lung nodule reference library that automatically retrieves historical cases that are relevant to the current case. The intelligent lung nodule reference library is built on top of our state-of-the-art deep learning-based lung nodule detection, segmentation and characterization system.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8507
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A Component-Based AI Engine Platform for Medical Workflow
Xu Chen (Winning Health)
As deep learning techniques have been applied to the field of healthcare, more and more AI-based medical systems continue to come forth, which are accompanied by new heterogeneity, complexity and security risks. In the real-world we've seen ...Read More

As deep learning techniques have been applied to the field of healthcare, more and more AI-based medical systems continue to come forth, which are accompanied by new heterogeneity, complexity and security risks. In the real-world we've seen this sort of situation lead to demand constraints, hindering AI applications development in China's hospitals. First, we'll share our experience in building a unified GPU accelerated AI engine system to feed component-based functionality into the existing workflow of clinical routine and medical imaging. Then, we'll demonstrate in a pipeline of integrating the different types of AI applications (detecting lung cancer, predicting childhood respiratory disease and estimating bone age) as microservice to medical station, CDSS, PACS and HIS system to support medical decision-making of local clinicians. On this basis, we'll describe the purpose of establishing an open and unified, standardized, legal cooperation framework to help AI participants to enter the market in China to build collaborative ecology.

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Keywords:
AI in Healthcare, Product & Building Design, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8895
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Computational Precision Medicine - How Healthcare May Look Like in 10 years Thanks to GPUs
Alejandro Frangi (CISTIB / The University of Sheffield)
This talk will overview the fields of Personalised Computational Medicine and In Silico Clinical Trials, which are revolutionizing Medicine and Medical Product Development. This talk will introduce these concepts, provide examples of how they ca ...Read More

This talk will overview the fields of Personalised Computational Medicine and In Silico Clinical Trials, which are revolutionizing Medicine and Medical Product Development. This talk will introduce these concepts, provide examples of how they can transform healthcare, and emphasize why artificial intelligence and machine learning are relevant to them. We will also explain the limitations of these approaches and why it is paramout to engage in both phenomenological (data-driven) and mechanistic (principle-driven) modelling. Both areas are in desperate need for better infrastructures -sofrware and hardaware- giving access to computational and storage resources. The talk will be thought-provoking and eye-opening as to opportunities in this space for researchers and industries alike.

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Keywords:
AI in Healthcare, Deep Learning and AI, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8887
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Deep Imaging: Quantitative Biomarkers for Clinical Decision Making
Razvan Ionasec (Siemens Healthineers)
The transformation towards value-based healthcare needs inventive ways to lower cost and increase patient health outcomes. Artificial intelligence is vital for realizing value-based care. Turning medical images into biomarkers helps to increase ...Read More

The transformation towards value-based healthcare needs inventive ways to lower cost and increase patient health outcomes. Artificial intelligence is vital for realizing value-based care. Turning medical images into biomarkers helps to increase effectiveness of care.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8412
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Deep Learning Improves Neuroimaging: Faster, Safer, Cheaper and Smarter
Enhao Gong (Stanford University, Subtle Medical), Greg Zaharchuk (Stanford University)
We will introduce deep learning applications in clinical neuroimaging (using MRI, CT, PET, etc.) and recent breakthrough results from Stanford and Subtle Medical. Perspectives and feedbacks of applying AI technologies in neuroimaging are shared, ...Read More

We will introduce deep learning applications in clinical neuroimaging (using MRI, CT, PET, etc.) and recent breakthrough results from Stanford and Subtle Medical. Perspectives and feedbacks of applying AI technologies in neuroimaging are shared, from expert radiologists and deep learning experts. How Deep Learning/AI is changing clinical neuroimaging practice * How will deep learning be applied in radiology workflow right now and in the future * Practical concerns and perspectives from radiologists How Deep Learning assists smarter neuroimaging decision making * Multi-scale 3D network enables lesion outcome prediction for stroke * More accurate lesion segmentation in neuroimaging How Deep Learning enables safer and cheaper neuroimaging screening * Deep Learning and GAN enables >95% reduction in radiation for functional medical imaging * Deep Learning enables 90% reduction in chemical (Gadolinium) contrast agent usage in contrast enhanced MRI How Deep Learning accelerate neuroimaging * Further acceleration and improved MRI reconstruction using deep learning * Deep Generative Adversarial Network for Compressed Sensing

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8647
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Back to the Essense of Medicine, Answer Clinical Questions -- Medical Imaging in AI Era
Xiaodong Tao (iFlytek)
iFLYTEK Health's mission is to use the most advanced artificial intelligence technologies to revolutionize healthcare industry to help doctors provide quality care to more patients with higher efficiency. Developed upon iFLYTEK's world c ...Read More

iFLYTEK Health's mission is to use the most advanced artificial intelligence technologies to revolutionize healthcare industry to help doctors provide quality care to more patients with higher efficiency. Developed upon iFLYTEK's world class hardware/software technologies in voice recognition and voice synthesization, iFLYTEK's products can help reduce doctors' burden in writing medical records and free their time to focus more on caring patients. These technologies can also reduce errors and improve completeness and accuracy of medical records, therefore support advanced intelligence applications based on complete patient data. Automated image analysis tools can help doctors find abnormalities in images with confidence, especially for the inexperienced doctors from lower tier hospitals. Clinical Decision Support (CDS) system is based on authoritative medical literature, large amount of expert knowledge, and real cases to improve primary doctors' ability of accurate diagnosis using complete and accurate patient information.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8302
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Deep Learning and Use of GPUs in Mammography
Hsi-Ming Chang (CureMetrix)
Discuss the difficulties in digital mammography, and the computational challenges we encountered while adapting deep learning algorithms, including GAN, to digital mammography. Learn how we address those computational issues, and get the informa ...Read More

Discuss the difficulties in digital mammography, and the computational challenges we encountered while adapting deep learning algorithms, including GAN, to digital mammography. Learn how we address those computational issues, and get the information of our benchmarking results using both consumer and enterprise grade GPUs.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8482
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From Promising Algorithms to Clinical Practice: Next Generation of Challenges
Wiro Niessen (Medical Image Computing and Computer Assisted Interventions (MICCAI))
There is large promise in machine learning methods for the automated analysis of medical imaging data for supporting disease detection, diagnosis and prognosis. These examples include the extraction of quantitative imaging biomarkers that are re ...Read More

There is large promise in machine learning methods for the automated analysis of medical imaging data for supporting disease detection, diagnosis and prognosis. These examples include the extraction of quantitative imaging biomarkers that are related to presence and stage of disease, radiomics approaches for tumor classification and therapy selection, and deep learning methods for directly linking imaging data to clinically relevant outcomes. However, the translation of such approaches requires methods for objective validation in clinically realistic settings or clinical practice. In this talk, I will discuss the role of next generation challenges for this domain.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8992
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Frontiers of AI in Medical Imaging: Overcoming Current Challenges and Moving Beyond Classification
Imon Banerjee (Stanford University), Daniel Rubin (Stanford University)
Learn about the key types of clinical use cases for AI methods in medical imaging beyond simple image classification that will ultimately improve medical practice, as well as the critical challenges and progress in applying AI to these applicati ...Read More

Learn about the key types of clinical use cases for AI methods in medical imaging beyond simple image classification that will ultimately improve medical practice, as well as the critical challenges and progress in applying AI to these applications. We''ll first describe the types of medical imaging and the key clinical applications for deep learning for improving image interpretation. Next, we''ll describe recent developments of word-embedding methods to leverage narrative radiology reports associated with images to generate automatically rich labels for training deep learning models and a recent AI project that pushes beyond image classification and tackles the challenging problem of clinical prediction. We''ll also describe emerging methods to leverage multi-institutional data for creating AI models that do not require data sharing and recent innovative approaches of providing explanation about AI model predictions to improve clinician acceptance.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8295
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Medical Imaging with TensorFlow
Josh Gordon (Google)
Dive in to recent work in medical imaging, where TensorFlow is used to spot cancerous cells in gigapixel images, and helps physicians to diagnose disease. During this talk, we''ll introduce concepts in Deep Learning, and show concrete co ...Read More

Dive in to recent work in medical imaging, where TensorFlow is used to spot cancerous cells in gigapixel images, and helps physicians to diagnose disease. During this talk, we''ll introduce concepts in Deep Learning, and show concrete code examples you can use to train your own models. In addition to the technology, we''ll cover problem solving process of thoughtfully applying it to solve a meaningful problem. We''ll close with our favorite educational resources you can use to learn more about TensorFlow.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8919
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Accelerating Bioinformatics: End-to-End Computation of NASA GeneLab Data with GPU Data Frame
Jacqueline Cenci-McGrody (NVIDIA), Venkat Krishnamurthy (MapD Technologies)
Protecting crew health is a critical concern for NASA in preparation of long duration, deep-space missions like Mars. Spaceflight is known to affect immune cells. Splenic B-cells decrease during spaceflight and in ground-based physiological mode ...Read More

Protecting crew health is a critical concern for NASA in preparation of long duration, deep-space missions like Mars. Spaceflight is known to affect immune cells. Splenic B-cells decrease during spaceflight and in ground-based physiological models. The key technical innovation presented by our work is end-to-end computation on the GPU with the GPU Data Frame (GDF), running on the DGXStation, to accelerate the integration of immunoglobulin gene-segments, junctional regions, and modifications that contribute to cellular specificity and diversity. Study results are applicable to understanding processes that induce immunosuppressionlike cancer therapy, AIDS, and stressful environments here on earth.

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Keywords:
AI in Healthcare, Performance Optimization, Bioinformatics & Genomics, GTC Silicon Valley 2018 - ID S8528
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Customizable Ultrasound Imaging in Real-Time Using a GPU-Accelerated Beamformer
Dongwoon Hyun (Stanford University)
Learn how researchers at Stanford University are leveraging the power of GPUs to improve medical ultrasound imaging. Ultrasound imaging is a powerful diagnostic tool that can provide clinicians with feedback in real time. Until recently, ultraso ...Read More

Learn how researchers at Stanford University are leveraging the power of GPUs to improve medical ultrasound imaging. Ultrasound imaging is a powerful diagnostic tool that can provide clinicians with feedback in real time. Until recently, ultrasound beamforming and image reconstruction has been performed using dedicated hardware in order to achieve the high frame rates necessary for real-time diagnostic imaging. Though many sophisticated techniques have been proposed to further enhance the diagnostic utility of ultrasound images, computational and hardware constraints have made translation to the clinic difficult. We have developed a GPU-accelerated software beamforming toolbox that enables researchers to implement custom real-time beamforming on any computer with a CUDA-capable GPU, including commercial ultrasound scanners. In this session, we will: 1) briefly introduce the basics of ultrasound beamforming, 2) present our software beamforming toolbox, and 3) show videos demonstrating its capabilities from a clinical study of echocardiography, as well as an implementation of a novel speckle removing beamformer that utilizes deep fully convolutional neural networks.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8279
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Highly Accurate Brain Stroke Diagnosis System and Generative Stroke Lesion Model
Junghwan Cho (CAIDE Systems, Inc)
Learn CAIDE Systems'' unique diagnosis system with highly accurate prediction and delineation of brain stroke lesion. We''ll present how we increase sensitivity in medical diagnosis system and how we develop a state-of-the-art ge ...Read More

Learn CAIDE Systems'' unique diagnosis system with highly accurate prediction and delineation of brain stroke lesion. We''ll present how we increase sensitivity in medical diagnosis system and how we develop a state-of-the-art generative deep learning model for acquiring segmented stroke lesion CT images, and demonstrate our market-ready product: a diagnostic tool as well as a medical deep learning platform. We trained our diagnostic system using CT image data from thousands of patients with brain stroke and tested to see commercial feasibility of use for hospitals and mobile ambulances.  

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8428
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Learning Normalized Inputs for Iterative Estimation in Medical Image Segmentation
Michal Drozdzal (Montreal Institute for Learning Algorithms)
In medical imaging, acquisition procedures and imaging signals vary across different modalities and, thus, researchers often treat them independently, introducing different models for each imaging modality. To mitigate the number of modality-spe ...Read More

In medical imaging, acquisition procedures and imaging signals vary across different modalities and, thus, researchers often treat them independently, introducing different models for each imaging modality. To mitigate the number of modality-specific designs, we introduced a simple yet powerful pipeline for medical image segmentation that combines fully convolutional networks (FCNs) with fully convolutional residual networks (FC-ResNets). FCNs are used to obtain normalized images, which are then iteratively refined by means of a FC-ResNet to generate a segmentation prediction. We''ll show results that highlight the potential of the proposed pipeline, by matching state-of-the-art performance on a variety of medical imaging modalities, including electron microscopy, computed tomography, and magnetic resonance imaging.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8251
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Deep Learning for Shallow Sequencing
Yonatan Israeli (NVIDIA)
The NVIDIA Genomics Group has developed a deep learning platform to transform noisy, low-quality DNA sequencing data into clean, high-quality data. Hundreds of DNA sequencing protocols are used to profile phenomena such as protein-DNA binding an ...Read More

The NVIDIA Genomics Group has developed a deep learning platform to transform noisy, low-quality DNA sequencing data into clean, high-quality data. Hundreds of DNA sequencing protocols are used to profile phenomena such as protein-DNA binding and DNA accessibility. For example, the ATAC-seq protocol identifies open genomic sites by sequencing open DNA fragments; genome-wide fragment counts provide a profile of DNA accessibility. Recent advances enable profiling from smaller patient samples than previously possible. To reduce sequencing cost, we developed a convolutional neural network that denoises data from a small number of DNA fragments, making the data suitable for various downstream tasks. Our platform aims to accelerate adoption of DNA sequencers by minimizing data requirements.

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Keywords:
AI in Healthcare, Bioinformatics & Genomics, GTC Silicon Valley 2018 - ID S8602
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Accelerating Nanopore Sequencing Using AI and Volta
Chuck Seberino (Roche Sequencing Solutions)
Nanopore sequencing is a breakthrough technology that marries cutting edge semiconductor processes together with biochemistry, achieving fast, scalable, single molecule DNA sequencing. The challenge is real-time processing of gigabytes of data p ...Read More

Nanopore sequencing is a breakthrough technology that marries cutting edge semiconductor processes together with biochemistry, achieving fast, scalable, single molecule DNA sequencing. The challenge is real-time processing of gigabytes of data per second in a compact benchtop instrument. GPUDirect, together with the cuDNN library, enables Roche to maximize the effectiveness of Tesla V100 GPUs in their next generation sequencing instrument. Attendees will learn how these pieces come together to build a streaming AI inference engine to solve a signal processing workflow. Analysis and performance comparisons of the new TensorCore units, available on Volta hardware, will be included.cal cuDNN API

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Keywords:
AI in Healthcare, Bioinformatics & Genomics, GTC Silicon Valley 2018 - ID S8947
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CUDA Based Stitching of Teravoxel Microscopy Images
Massimo Bernaschi (National Research Council of Italy)
Learn how to use (multi) GPU and CUDA to speed up the process of stitching very large images (up to TeraBytes in size). Image stitching is the process of combining multiple photographic images with overlapping fields of view to produce a segment ...Read More

Learn how to use (multi) GPU and CUDA to speed up the process of stitching very large images (up to TeraBytes in size). Image stitching is the process of combining multiple photographic images with overlapping fields of view to produce a segmented panorama or high-resolution image. Image stitching is widely used in many important fields, like high resolution photo mosaics in digital maps and satellite photos or medical images. Motivated by the need to combine images produced in the study of the brain, we developed and released for free the TeraStitcher tool that we recently enhanced with a CUDA plugin that allows an astonishing speedup of the most computing intensive part of the procedure. The code can be easily adapted to compute different kinds of convolution. We describe how we leverage shuffle operations to guarantee an optimal load balancing among the threads and CUDA streams to hide the overhead of moving back and forth images from the CPU to the GPU when their size exceeds the amount of available memory. The speedup we obtain is such that jobs that took several hours are now completed in a few minutes.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8182
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The Early Detection of Pancreatic Cancer Using Deep Learning: Preliminary Observations
Elliot Fishman (Johns Hopkins Hospital)
This talk will present the challenges and opportunities in developing a deep learning program for use in medical imaging. It will present a hands on approach to the challenges that need to be overcome and the need for a multidisciplinary approac ...Read More

This talk will present the challenges and opportunities in developing a deep learning program for use in medical imaging. It will present a hands on approach to the challenges that need to be overcome and the need for a multidisciplinary approach to help define the problems and potential solutions. The role of highly curated data for training the algorithms and the challenges in creating such datasets is addressed. The annotation of data becomes a key point in training and testing the algorithms. The role of experts in computer vision, and radiology will be addressed and how this project can prove to be a roadmap for others planning collaborative efforts will be addressed Finally I will discuss the early results of the Felix project whose goal is nothing short of the early detection of pancreatic cancer to help improve detection and ultimately improve patient outcomes.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S81004
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Performance Improvements for CUDA Accelerated Real-Time Diagnostic Ultrasound Medical Imaging Motion Tracking
Ismayil Guracar (Siemens Medical Solutions, USA Inc. Ultrasound Group)
Motion tracking with motion compensation is an important component of modern advanced diagnostic ultrasonic medical imaging with microbubble contrast agents. Search-based on sum of absolute differences a well-known technique for motion estimatio ...Read More

Motion tracking with motion compensation is an important component of modern advanced diagnostic ultrasonic medical imaging with microbubble contrast agents. Search-based on sum of absolute differences a well-known technique for motion estimation is very amenable to efficient implementations, which exploit the fine grained parallelism inherent in GPUs. We''ll demonstrate a real-world application for motion estimation and compensation in the generation of real-time maximum intensity projections over time to create vascular roadmaps in medical images of organs, such as the liver with ultrasound contrast agents. We''ll provide CUDA kernel code examples which make this application possible as well as performance measurements demonstrating the value of instruction-level parallelism and careful control of memory access patterns for kernel performance improvement. We hope to provide insight to CUDA developers interested in motion estimation and compensation as well as general insight into kernel performance optimization relevant for any CUDA developer.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8233
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Continuously Learning AI Pathologist : A Smart Microscope that can Automatically Screen Different Biological Specimen
Tathagato Rai Dastidar (SigTuple Technologies Pvt Ltd)
Clinical laboratories play a crucial role in healthcare ecosystem - the laboratories are pivotal and act as a screening sub-system by providing early inference in disease and abnormality diagnosis. An estimated 70% of clinical decisions regardin ...Read More

Clinical laboratories play a crucial role in healthcare ecosystem - the laboratories are pivotal and act as a screening sub-system by providing early inference in disease and abnormality diagnosis. An estimated 70% of clinical decisions regarding prevention, diagnosis and treatment involve lab tests. Surprisingly, 60% of the inferencing done at a clinical laboratory can be performed by one "wonder-tool" - microscope. Microscopy has helped pathologists assess and analyse the patients for over several centuries. The key hurdles in the microscopic examination are the amount of time that the pathologists have to spend in manual analysis and the need for the pathologists to be co-located with the specimen. In this talk, we introduce SigTuple's AI powered smart microscope that can automatically learn, analyse and summarize the inferences of several hundred abnormalities across different biological specimen (blood, urine and semen). It also utilizes the power of GPU computing on cloud to provide higher order analysis of the samples and acts as a tele-pathology enabler by providing pathologists the power to view or review any analysis or report from any part of the world.

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Keywords:
AI in Healthcare, Pathology, GTC Silicon Valley 2018 - ID S8591
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GE's Evolution from HPC to AI in Healthcare
Keith Bigelow (GE Healthcare Waukesha), Erik Steen (GE Healthcare)
For more than a decade, GE has partnered with Nvidia in Healthcare to power our most advanced modality equipment, from CT to Ultrasound. Part 1 of this session will offer an introduction to the deep learning efforts at GEHC, the platform we' ...Read More

For more than a decade, GE has partnered with Nvidia in Healthcare to power our most advanced modality equipment, from CT to Ultrasound. Part 1 of this session will offer an introduction to the deep learning efforts at GEHC, the platform we're building on top of NGC to accelerate new algorithm development, and then a deep dive into a case study of the evolution of our cardiovascular ultrasound scanner and the underlying extensible software stack. It will contain 3 main parts as follows: (a) Cardiovascular ultrasound imaging from a user perspective. Which problems we need to solve for our customers. Impact of Cardiovascular disease in a global perspective (b) An introduction to the Vivid E95 and the cSound platform , GPU based real time image reconstruction & visualization. How GPU performance can be translated to customer value and outcomes and how this has evolved the platform during the last 2 ½ years. (c) Role of deep learning in cardiovascular ultrasound imaging, how we are integrating deep learning inference into our imaging system and preliminary results from automatic cardiac view detection.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8849
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Accelerating Medical Device Development in Medical Imaging
Alejandro Frangi (CISTIB / The University of Sheffield)
TBA ...Read More

TBA

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8993
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Deep Learning Brings Disruptive Changes to Ophthalmology
Aaron Lee (University of Washington)
Hear about how GPU technology is disrupting the way your eye doctor works and how ophthalmic research is performed today. The rise of Electronic Medical Records in medicine has created mountains of Big Data particularly in ophthalmology where ma ...Read More

Hear about how GPU technology is disrupting the way your eye doctor works and how ophthalmic research is performed today. The rise of Electronic Medical Records in medicine has created mountains of Big Data particularly in ophthalmology where many discrete quantitative clinical elements like visual acuity can be tied to rich imaging datasets. In this session, we will explore the transformative nature that GPU acceleration has played in accelerating clinical research and show real-life examples of deep learning applications to ophthalmology in creating new steps forward in automated diagnoses, image segmentation, and computer aided diagnoses.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8866
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Cascaded 3D Fully Convolutional Networks for Medical Image Segmentation
Holger Roth (Nagoya University)
We'll show how recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce dense voxel-wise predictions of volumetric images. FCNs can be trained to automatically segment 3D medical images, such as computed tomo ...Read More

We'll show how recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce dense voxel-wise predictions of volumetric images. FCNs can be trained to automatically segment 3D medical images, such as computed tomography (CT) scans based on manually annotated anatomies like organs and vessels. The presented methods achieve competitive segmentation results while avoiding the need for handcrafting features or training class-specific models, in a clinical setting. We'll explain a two-stage, coarse-to-fine approach that will first use a 3D FCN based on the 3D U-Net architecture to roughly define a candidate region. This candidate region will then serve as input to a second 3D FCN to do a fine prediction. This cascaded approach reduces the number of voxels the second FCN has to classify to around 10 percent of the original 3D medical image, and therefore allows it to focus on more detailed segmentation of the organs and vessels. Our experiments will illustrate the promise and robustness of current 3D FCN based semantic segmentation of medical images, achieving state-of-the-art results on many datasets. Code and trained models will be made available.

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Keywords:
AI in Healthcare, Deep Learning and AI Frameworks, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8532
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GPU-Enabled Ultrasound Imaging for Real-Time, Fully-Flexible Data Processing
Christoph Hennersperger (Technical University of Munich | Trinity College Dublin)
Explore how parallelized programming and DL can radically impact medical ultrasound imaging. In this session, we will describe how the processing of ultrasound signals can be implemented not only providing real-time capabilities, but also a flex ...Read More

Explore how parallelized programming and DL can radically impact medical ultrasound imaging. In this session, we will describe how the processing of ultrasound signals can be implemented not only providing real-time capabilities, but also a flexible environment for research and innovative new products. In this view, we will i) demonstrate 2D and 3D real-time imaging using open hardware platforms, and ii) provide an overview, how both radical parallelization and DL can be integrated within processing pipelines, providing new applications and improved image quality at unprecedented speed.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8764
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Harnessing AI: Creating a Healthcare AI Ecosystem
Keith Dreyer (Partners HealthCare)
In this session, attendees will learn how to develop an AI Learning Platform for healthcare, develop initial(imaging) AI applications in specific care areas, and embed AI into devices creating "intelligent imaging systems". ...Read More

In this session, attendees will learn how to develop an AI Learning Platform for healthcare, develop initial(imaging) AI applications in specific care areas, and embed AI into devices creating "intelligent imaging systems".

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8991
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AI Models to Clinical Practice: Open AI Marketplace for Diagnostic Imaging
Woojin Kim (Nuance Communications), Arman Sharafshahi (Nuance Communications)
Learn about the importance of clinical domain expertise in AI algorithm/model development and incorporation into clinical workflow, specifically in medical imaging, from a radiologist. With growing media attention, there is much fear, hype, and ...Read More

Learn about the importance of clinical domain expertise in AI algorithm/model development and incorporation into clinical workflow, specifically in medical imaging, from a radiologist. With growing media attention, there is much fear, hype, and hope when it comes to using DL in radiology. We will present through examples why it is essential to incorporate clinical domain expertise when developing DL models. We will demonstrate various ways AI can augment the radiologists both in image interpretation as well as beyond within the overall workflow. In the second portion of this talk, we will present the gap between developing a great AI model in isolation and having it become part of daily medical practice. From integration and hospital connectivity to algorithm serving at scale to meet growing demand, we will show how an AI Marketplace can create the ecosystem that allows AI to flourish.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8871
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Computer-Augmented Healthcare: Opportunities and Challenges
Gregory Hager (The Malone Center for Engineering in Healthcare, Johns Hopkins University)
The Role of Data in Achieving Precision and Value in Healthcare The goal of healthcare is to provide the most effective treatment to every patient in the most efficient way. Data plays a key role in every aspect of this process from decision sup ...Read More

The Role of Data in Achieving Precision and Value in Healthcare The goal of healthcare is to provide the most effective treatment to every patient in the most efficient way. Data plays a key role in every aspect of this process from decision support systems that provide a clinician with the right information at the right time, to scheduling algorithms that predict patient flow and schedule accordingly, to analytics to coach and support patients in achieving or maintaining a healthy lifestyle. Achieving the vision of a data-informed healthcare system will require fundamental advances in many areas including causal inference, inference on complex, high-dimensional and heterogeneous data, missing data, process modeling, bias reduction, statistical validation, and model adaptation, to name a few. In this talk, I will illustrate some of these challenges through concrete examples within the Malone Center.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8891
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AI DR Screening for Chronic Diseases
Emma Xu (Airdoc)
Diabetic retinopathy, also known as diabetic eye disease, is a major complication of diabetes, which damage occurs to the retina due to diabetes mellitus and is a leading cause of blindness. AirDoc's product Dirctor, Emma Xu and Professor Yo ...Read More

Diabetic retinopathy, also known as diabetic eye disease, is a major complication of diabetes, which damage occurs to the retina due to diabetes mellitus and is a leading cause of blindness. AirDoc's product Dirctor, Emma Xu and Professor You Li of Shanghai Changzheng Hospital, will share how AirDoc, the leading Intelligent Medical startup in China, leverages Nvidia's GPU and Deep Learning to improve the DR diagnose with Automatic left/right eye recognition, Automatic detection of the location and numbers, Automatic DR staging, Fast recognition speed, Patient Information Management for real-time screening statistics and usage management.

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Keywords:
AI in Healthcare, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8940
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Accelerated Analytics
Presentation
Media
Accelerating Cross-Validation in Spark Using GPU
Minsik Cho (IBM Research)
Learn how to utilize GPUs better to accelerate cross-validation in Spark, which is widely used in many bigdata analytics/machine learning applications. ...Read More

Learn how to utilize GPUs better to accelerate cross-validation in Spark, which is widely used in many bigdata analytics/machine learning applications.

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Keywords:
Accelerated Analytics, Deep Learning and AI, GTC Silicon Valley 2017 - ID S7117
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cuMF_sgd: Fast and Scalable Matrix Factorization on GPUs
Wei Tan (IBM T. J. Watson Research Center)
Matrix factorization (MF) has been widely used in recommender systems, topic modeling, word embedding, and more. Stochastic gradient descent (SGD) for MF is memory bound. Meanwhile, single-node CPU systems with caching performs well only for sma ...Read More

Matrix factorization (MF) has been widely used in recommender systems, topic modeling, word embedding, and more. Stochastic gradient descent (SGD) for MF is memory bound. Meanwhile, single-node CPU systems with caching performs well only for small datasets. Distributed systems have higher aggregated memory bandwidth but suffer from relatively slow network connections. This observation inspires us to accelerate MF by utilizing GPUs's high memory bandwidth and fast intra-node connection. We present cuMF_SGD, a CUDA-based SGD solution for large-scale MF problems. On a single CPU, we design two workload schedule schemes, i.e., batch-Hogwild! and wavefront-update, that fully exploit the massive amount of cores. batch-Hogwild! as a vectorized version of Hogwild! especially overcomes the issue of memory discontinuity. On three datasets with only one Maxwell or Pascal GPU, cuMF_SGD runs 3.1 to 28.2x as fast compared with state-of-art CPU solutions on 1 to 64 CPU nodes.

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Keywords:
Accelerated Analytics, GTC Silicon Valley 2017 - ID S7127
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Anomaly Detection for Network Intrusions Using Deep Learning
Adam Gibson (Skymind), David Kale (Skymind)
We'll describe how deep learning can be applied to detect anomalies, such as network intrusions, in a production environment. In part one of the talk, we'll build an end-to-end data pipeline using Hadoop for storage, Streamsets for data ...Read More

We'll describe how deep learning can be applied to detect anomalies, such as network intrusions, in a production environment. In part one of the talk, we'll build an end-to-end data pipeline using Hadoop for storage, Streamsets for data flow, Spark for distributed GPUs, and Deeplearning for anomaly detection. In part two, we'll showcase a demo environment that demonstrates how a deep net uncovers anomalies. This visualization will illustrate how system administrators can view malicious behavior and prioritize efforts to stop attacks. It's assumed that registrants are familiar with popular big data frameworks on the JVM.

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Keywords:
Accelerated Analytics, AI Startup, Federal, Deep Learning and AI, GTC Silicon Valley 2017 - ID S7143
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Leverage GPU Acceleration for your Program on Apache Spark
Kazuaki Ishizaki (IBM Research - Tokyo)
Learn how to transparently and effectively leverage NVIDIA GPUs from your Spark program on Apache Spark. We'll provide an overview on how common programmers can leverage GPUs on Apache Spark using our two approaches. One is that a Ninja programmer p ...Read More
Learn how to transparently and effectively leverage NVIDIA GPUs from your Spark program on Apache Spark. We'll provide an overview on how common programmers can leverage GPUs on Apache Spark using our two approaches. One is that a Ninja programmer provides an optimized GPU kernel to develop Spark libraries, which is implemented as a drop-in module to Spark. This allows common programmers to transparently use GPUs by calling these libraries. The other is that enhanced Spark runtime transparently generates GPU code from a Spark program. Our two approaches use the following two components for ease of leveraging GPUs and for achieving high performance. One component is a GPU driver for managing GPU devices, performing data copy, and launching GPU kernels. The other is column-oriented data structure for Spark's data structures, which is suitable for GPU. See experimental results on acceleration of Spark Applications with two approaches using NVIDIA GPUs.  Back
 
Keywords:
Accelerated Analytics, GTC Silicon Valley 2017 - ID S7168
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Streaming Graph Analytics on the GPU
Oded Green (Georgia Tech)
We'll present cuSTINGER - the first dynamic graph data structure for the GPU. We will start off by discussing the internals of the data structure. We'll compare cuSTINGER with CSR, a widely used static graph and matrix data representation, and show ...Read More
We'll present cuSTINGER - the first dynamic graph data structure for the GPU. We will start off by discussing the internals of the data structure. We'll compare cuSTINGER with CSR, a widely used static graph and matrix data representation, and show how that our dynamic graph data structure is within a few percent of static graph structures. We'll show additional performance results: time to initialize the data structure, time required to modify the graph (due to updates), and the update rate (which represents how many update per second cuSTINGER can deal with). Currently, cuSTINGER can sustain over 10 million updates per second. Lastly, we'll show a novel algorithm for counting triangles in a streaming environment which sustains million of updates per second.  Back
 
Keywords:
Accelerated Analytics, Algorithms and Numerical Techniques, GTC Silicon Valley 2017 - ID S7220
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Spectral Clustering of Large Networks
Alexandre Fender (NVIDIA), Maxim Naumov (NVIDIA)
We'll explore techniques for expressing graph clustering as an eigenvalue problem. Attendees will learn how to express different metrics, including minimum balanced cut, modularity, and Jaccard, through associated matrices and how to use their eigen ...Read More
We'll explore techniques for expressing graph clustering as an eigenvalue problem. Attendees will learn how to express different metrics, including minimum balanced cut, modularity, and Jaccard, through associated matrices and how to use their eigenvectors to find the clustering of the graph into multiple partitions. We'll also show how to take advantage of efficient implementation of Lanczos and LOBPCG eigenvalue solvers and k-means algorithm on the GPU to compute clustering using our general framework. Finally, we'll highlight the performance and quality of our approach versus existing state-of-the-art techniques.  Back
 
Keywords:
Accelerated Analytics, Algorithms and Numerical Techniques, HPC and Supercomputing, GTC Silicon Valley 2017 - ID S7241
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Bayesian Inference and Markov Chain Monte Carlo Algorithms on GPUs
David Draper (UC Santa Cruz), Alexander Terenin (UC Santa Cruz)
We'll discuss the Bayesian statistical paradigm and Markov Chain Monte Carlo (MCMC) algorithms - the cornerstone of modern Bayesian computation. Scalable MCMC for big datasets and complex models is currently an open research question. Using GPUs pro ...Read More
We'll discuss the Bayesian statistical paradigm and Markov Chain Monte Carlo (MCMC) algorithms - the cornerstone of modern Bayesian computation. Scalable MCMC for big datasets and complex models is currently an open research question. Using GPUs provides a promising and largely unexplored avenue for accelerating these algorithms, but is nontrivial, because MCMC is inherently sequential and has traditionally been considered difficult to parallelize. We'll show how Gibbs sampling, a widely used MCMC algorithm, can be effectively parallelized on GPUs for a large class of exchangeable hierarchical Bayesian models. Participants will learn the mathematical and hardware/software challenges in bringing GPUs to the Bayesian community. Background in Bayesian statistics or MCMC is not assumed.  Back
 
Keywords:
Accelerated Analytics, Federal, Algorithms and Numerical Techniques, GTC Silicon Valley 2017 - ID S7263
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Automatic Compiler-Based Optimization of Graph Analytics for the GPU
Sreepathi Pai (The University of Texas at Austin)
Learn how to use IrGL, our newly developed language and compiler, to obtain high-speed graph algorithm implementations without writing a lot of low-level NVIDIA CUDA. IrGL can be used for parallel graph algorithm research, graph analytics, and graph ...Read More
Learn how to use IrGL, our newly developed language and compiler, to obtain high-speed graph algorithm implementations without writing a lot of low-level NVIDIA CUDA. IrGL can be used for parallel graph algorithm research, graph analytics, and graph database query processing. IrGL performance for graph algorithms meets or exceeds the performance of low-level handwritten CUDA code because our optimizing compiler automatically tackles three key challenges encountered in writing graph algorithms -- atomics, load imbalance due to serialization of loops, and kernel launch throughput -- freeing up the programmer to focus on higher-level optimizations. We'll introduce the IrGL language, its compiler, and how they can use IrGL to target problems with irregular data-parallelism.  Back
 
Keywords:
Accelerated Analytics, Programming Languages, Performance Optimization, GTC Silicon Valley 2017 - ID S7267
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Accelerating Document Retrieval and Ranking for Cognitive Applications
Tim Kaldewey (IBM Watson), David Wendt (IBM)
Based on a comprehensive performance study of Watson workloads, we'll deep dive into optimizing critical retrieve and rank functions using GPU acceleration. The performance of cognitive applications like answering natural language questions ...Read More

Based on a comprehensive performance study of Watson workloads, we'll deep dive into optimizing critical retrieve and rank functions using GPU acceleration. The performance of cognitive applications like answering natural language questions heavily depends on quickly selecting the relevant documents needed to generate a correct answer. While analyzing the question to determine appropriate search terms, weights, and relationships is relatively quick, retrieving and ranking a relevant subset from millions of documents is a time-consuming task. Only after completing it can any advanced natural language processing algorithms be effective.

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Keywords:
Accelerated Analytics, Federal, GTC Silicon Valley 2017 - ID S7321
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Sentiment Analysis Through the Use of Unsupervised Deep Learning
Stephen McGough (Newcastle University)
It is estimated that 85% of worldwide data is held in unstructured/unlabelled formats - increasing at a rate of roughly 7 million digital pages per day. Exploiting these large datasets can open the door for providing policy makers, corporations, ...Read More

It is estimated that 85% of worldwide data is held in unstructured/unlabelled formats - increasing at a rate of roughly 7 million digital pages per day. Exploiting these large datasets can open the door for providing policy makers, corporations, and end-users with unprecedented knowledge for better planning, decision making, and new services. Deep learning and probabilistic topic modeling have shown great potential for analysing such datasets. This analysis helps in: discovering anomalies within these datasets, unravelling underlying patterns/trends, or finding similar texts within a dataset. We'll illustrate how we can use a combined unsupervised deep learning and topic modeling approach for sentiment analysis requiring minimal feature engineering or prior assumptions, and outperforming the state of the art approaches to sentiment analysis.

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Keywords:
Accelerated Analytics, Federal, Deep Learning and AI, GTC Silicon Valley 2017 - ID S7330
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Performant Deep Reinforcement Learning: Latency, Hazards, and Pipeline Stalls in the GPU Era ... and How to Avoid Them
Mark Hammond (Bonsai)
The headache of latency, hazards, and pipeline stalls has reared its head again, taking a new form in the GPU era. In the realm of deep reinforcement learning, stateful, interactive simulation-based workloads push this to the extreme, necessitating a ...Read More
The headache of latency, hazards, and pipeline stalls has reared its head again, taking a new form in the GPU era. In the realm of deep reinforcement learning, stateful, interactive simulation-based workloads push this to the extreme, necessitating a handoff to the simulator on every iteration - and that simulator may not even be running on the same machines as the deep reinforcement learning model! We'll explore lessons learned on how to avoid these performance degrading modern hazards. Attendees will learn tricks and techniques - including approaches to pool multiple concurrent simulations for use with single networks - that they can employ in their own systems to increase performance with their deep reinforcement learning workloads.  Back
 
Keywords:
Accelerated Analytics, AI Startup, Performance Optimization, Intelligent Machines and IoT, Deep Learning and AI, GTC Silicon Valley 2017 - ID S7359
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In the Blink of an Eye: High-Performance Analytics on NVIDIA GPUs
Partha Sen (Fuzzy Logix)
By combining the enormous storage capacity of massively parallel processing (MPP) databases and Hadoop platforms with GPU-based analytics, a new architecture of fast Accelerated Analytics--with the ability to scale--will be discussed. We'll focus on ...Read More
By combining the enormous storage capacity of massively parallel processing (MPP) databases and Hadoop platforms with GPU-based analytics, a new architecture of fast Accelerated Analytics--with the ability to scale--will be discussed. We'll focus on benchmarks and early customer results in the use of high-performance, parallelized analytics on NVIDIA chips. We'll show how this GPU environment can be linked to the millions or billions of rows of data in databases or Hadoop clusters. We'll also cover solutions to business problems that were previously considered unsolvable using conventional CPU-based analytics. Use cases from finance, retail, healthcare, and manufacturing industries will be described.  Back
 
Keywords:
Accelerated Analytics, AI Startup, Federal, Algorithms and Numerical Techniques, Finance, GTC Silicon Valley 2017 - ID S7369
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Efficient Maximum Flow Algorithm and Applications
Hugo Braun (Ecole Polytechnique), Nikolay Sakharnykh (NVIDIA)
Maximizing data flow is one of the most important graph problems and has numerous applications across various computational domains: transportation networks, power routing, image segmentation, social network clustering, and recommendation systems. Th ...Read More
Maximizing data flow is one of the most important graph problems and has numerous applications across various computational domains: transportation networks, power routing, image segmentation, social network clustering, and recommendation systems. There are many efficient algorithms that have been developed for this problem, most of them trying to minimize computational complexity. However, not all these algorithms map well to massively parallel architectures like GPUs. We'll present a novel GPU-friendly approach based on the MPM algorithm that achieves from 5 to 20 times speedup over the state-of-the-art multithreaded CPU implementation from Galois library on general graphs with various diameters. We'll also discuss some real-world applications of the maximum flow problem in computer vision for image segmentation and in data analytics to find communities in social networks.  Back
 
Keywords:
Accelerated Analytics, Algorithms and Numerical Techniques, GTC Silicon Valley 2017 - ID S7370
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Petabyte Data Pipelines: Massively Distributed SQL Data Warehouse on GPUs
Felipe Aramburu (BlazingDB), Rodrigo Aramburu (BlazingDB)
With exponential data growth and the end of Moore's law, enabling data warehouses to scale is a huge challenge. Storing a petabyte in a data warehouse is incredibly costly, and often times non-performant. BlazingDB opens up a whole new level of spee ...Read More
With exponential data growth and the end of Moore's law, enabling data warehouses to scale is a huge challenge. Storing a petabyte in a data warehouse is incredibly costly, and often times non-performant. BlazingDB opens up a whole new level of speed with GPU power, while using data lake technologies to store massive data sets. We'll demonstrate how BlazingDB leverages GPUs for writing and reading, where compression and data skipping are key, and then for SQL analytics, where sorting, aggregations, and joining see huge performance bumps. This demo will be performed on a Microsoft Azure N Series GPU cluster for processing and Azure File Store for cold storage, showing a fully functional BlazingDB cloud deployment processing a massive data set.  Back
 
Keywords:
Accelerated Analytics, AI Startup, Data Center and Cloud Infrastructure, GTC Silicon Valley 2017 - ID S7375
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Scaling Investigations: Next-Generation Visual Graph Analytics through GPU Cloud Streaming
Leo Meyerovich (Graphistry, Inc.)
Scaling visual investigations is a tough problem. Analysts in areas like cyber security, anti-fraud, ML model tuning, and network operations are struggling to see their data and how it connects. We'll discuss where visual graph analytics get ...Read More

Scaling visual investigations is a tough problem. Analysts in areas like cyber security, anti-fraud, ML model tuning, and network operations are struggling to see their data and how it connects. We'll discuss where visual graph analytics gets used and how Graphistry is dramatically streamlining the analyst experience. For example, when using visual graph models for exploring security event logs, we can load events around an incident and quickly determine the root cause, scope, and progression. We'll demonstrate how we solve three technical aspects of scaling visual graph analysis: streamlining investigation workflows, visualizing millions of events in the browser, and fast analytics. Core to our approach, our platform connects GPUs in the client to GPUs on the server. The result is an investigation experience that feels like a ""Netflix for data"" and can be used by anyone with a browser.

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Keywords:
Accelerated Analytics, Federal, Data Center and Cloud Infrastructure, GTC Silicon Valley 2017 - ID S7407
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Community Detection on the GPU
Mahantesh Halappanavar (Pacific Northwest National Laboratory), Antonino Tumeo (Pacific Northwest National Laboratory)
Community detection is a key kernel in the analysis of complex networks for a variety of fields. We'll present our implementation of a new GPU algorithm for community detection based on the Louvain Method. Our approach parallelizes the access to ind ...Read More
Community detection is a key kernel in the analysis of complex networks for a variety of fields. We'll present our implementation of a new GPU algorithm for community detection based on the Louvain Method. Our approach parallelizes the access to individual edges, enabling load balancing of networks with nodes of highly varying degrees. We're able to obtain speedups up to a factor of 270 compared to the sequential algorithm. The algorithm consistently outperforms other recent shared memory implementations and is only one order of magnitude slower than the current fastest parallel Louvain method running on a Blue Gene/Q supercomputer using more than 500K threads.  Back
 
Keywords:
Accelerated Analytics, Federal, Algorithms and Numerical Techniques, GTC Silicon Valley 2017 - ID S7423
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Real-Time Analytics Powered by GPU-Accelerated Databases
Chris Prendergast (Kinetica)
To stream data in real time, organizations need to figure out how to build an effective streaming pipeline that is easy to manage, low cost, scalable, and future-proofed. To address this challenge, enterprise architects have stitched together differe ...Read More
To stream data in real time, organizations need to figure out how to build an effective streaming pipeline that is easy to manage, low cost, scalable, and future-proofed. To address this challenge, enterprise architects have stitched together different tools, yielding decent results on predetermined analytics. But adding new data sources, new analytics, and scale can limit its long-term value. New solutions have emerged that can ingest many disparate datasets into one platform and open up net-new analytics. We'll describe a groundbreaking in-memory database technology powered by GPUs that enables high-speed data ingest and real-time data analytics. Drawing from real-world production use cases and live demos, we'll demonstrate how it's possible to perform advanced analytical queries across billions of rows of data in under a second.  Back
 
Keywords:
Accelerated Analytics, AI Startup, Intelligent Machines and IoT, Deep Learning and AI, Real-Time Graphics, GTC Silicon Valley 2017 - ID S7432
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How to Achieve Real-Time Analytics on a Data Lake Using GPUs
Mark Brooks (Kinetica)
The complexities associated with development and ongoing management of a data lake that aims to deliver real-time analytic response can be costly and overwhelming. To get real-time analytic response on live, streaming data, consider plugging a GPU-ac ...Read More
The complexities associated with development and ongoing management of a data lake that aims to deliver real-time analytic response can be costly and overwhelming. To get real-time analytic response on live, streaming data, consider plugging a GPU-accelerated database into your data lake. GPUs are often embedded in compute-intensive technologies like video games, cars, and mobile devices. They're now gaining traction in the data center. This talk will describe how a GPU-accelerated, scale-out, in-memory database brings orders of magnitude more compute power, with a significantly smaller hardware footprint, to provide unrivaled analytic capabilities. Get the latest information on GPUs, and how their multi-core architecture can process many computations efficiently and quickly, making them ideal for today's streaming datasets and IoT use cases.  Back
 
Keywords:
Accelerated Analytics, AI Startup, Intelligent Machines and IoT, Deep Learning and AI, Real-Time Graphics, GTC Silicon Valley 2017 - ID S7433
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SQream DB: Analyzing Customer Behavioral Data to Drive Revenue, the GPU Way
Arnon Shimoni (SQream Technologies)
We'll explore how we deployed a GPU-based analytics database in a telecom, and how it created value for the customer in just a few days. Discover how major enterprises have successfully harnessed next-generation database technology to understand and ...Read More
We'll explore how we deployed a GPU-based analytics database in a telecom, and how it created value for the customer in just a few days. Discover how major enterprises have successfully harnessed next-generation database technology to understand and use multi-terabytes of customer behavior data, using SQream DB powered by NVIDIA GPUs. We'll explore how a GPU solution can be applied to a real-world customer use case, and highlight the benefits and challenges of deploying a GPU-enabled solution. We'll also include actual performance benchmarks and screenshots from the deployed solution.  Back
 
Keywords:
Accelerated Analytics, GTC Silicon Valley 2017 - ID S7456
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Big Data, Little Cluster: Using a Small Footprint of GPU Servers to Interactively Query and Visualize Massive Datasets
Todd Mostak (MapD)
We'll discuss the approach to and advantages of using GPUs to not only power through large-scale database queries but also use the graphics pipeline of the GPU to rapidly and efficiently visualize the outputs of billions of rows of data. The applica ...Read More
We'll discuss the approach to and advantages of using GPUs to not only power through large-scale database queries but also use the graphics pipeline of the GPU to rapidly and efficiently visualize the outputs of billions of rows of data. The application of the GPU for both query and render results in a fast system for multi-terabyte scale analytic challenges. We'll cover the high-level benefits of the approach and delve into the technical details associated with GPU-powered databases, server side rendering, and other software refinements needed to squeeze the maximum amount of performance from this exceptional hardware platform.  Back
 
Keywords:
Accelerated Analytics, AI Startup, Federal, Real-Time Graphics, GTC Silicon Valley 2017 - ID S7475
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Apache Spark and GPUs for Scaling Deep Learning Libraries
Joseph Bradley (Databricks, Inc), Tim Hunter (Databricks, Inc)
Apache Spark has become a popular tool for data warehousing, ETL, and advanced analytics. Meanwhile, deep learning has become one of the most powerful classes of machine learning methods, in large part due to the computational power of modern machine ...Read More
Apache Spark has become a popular tool for data warehousing, ETL, and advanced analytics. Meanwhile, deep learning has become one of the most powerful classes of machine learning methods, in large part due to the computational power of modern machines with GPUs and specialized hardware. Spark and GPUs combine well for large deep learning workflows: Spark can handle ETL and data management, and it can distribute data parallel tasks to scale out across many GPUs.  Back
 
Keywords:
Accelerated Analytics, Federal, Deep Learning and AI, Data Center and Cloud Infrastructure, GTC Silicon Valley 2017 - ID S7510
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Using the Entire GPU: End-to-End Hardware Acceleration of SQL, Visualization, and Machine Learning
Bill Maimone (MapD)
The ability to use GPUs to power real-time analytics past the billion row threshold is already here. But what about a trillion rows? The technical challenges to overcome that hurdle are more complex and require a delicate balance of memory management ...Read More
The ability to use GPUs to power real-time analytics past the billion row threshold is already here. But what about a trillion rows? The technical challenges to overcome that hurdle are more complex and require a delicate balance of memory management, data serialization over the network, servers working in lockstep, and managing redundancy and single points of failure. We'll outline and demonstrate how MapD tackled this problem and, more importantly, how you can visualize the outputs of various queries.  Back
 
Keywords:
Accelerated Analytics, AI Startup, Federal, Data Center and Cloud Infrastructure, GTC Silicon Valley 2017 - ID S7511
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Eliminating the Regular Expression with Neural Networks
Tim Delisle (Datalogue)
Regular expressions are as old as computing itself. Our deep learning-based approaches aim to retire this tool from the modern data scientist's tool bag. The regular expression is often introduced to computer scientists as part of their early colleg ...Read More
Regular expressions are as old as computing itself. Our deep learning-based approaches aim to retire this tool from the modern data scientist's tool bag. The regular expression is often introduced to computer scientists as part of their early college education, often in their first discrete structures course. In this context, they are an incredible tool used to describe languages, grammars, and syntax. In practice though, developers all over the world use them to detect data types or parse certain structures. Even for common use cases such as email or phone validation, regular expressions that capture the full breadth of cases can become untenably large. We show how neural networks can learn approximation of regular expressions so that modern data scientists and developers never have to write one again.  Back
 
Keywords:
Accelerated Analytics, AI Startup, Deep Learning and AI, GTC Silicon Valley 2017 - ID S7515
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Extending Mahout-Samsara Linear Algebra DSL to Support GPU Clusters
Trevor Grant (IBM), Suneel Marthi (Redhat Inc)
Data scientists love tools like R and Scikit-Learn, as they offer a convenient and familiar syntax for analysis tasks. However, these systems are limited to operating serially on datasets that can fit on a single node and don't allow for distributed ...Read More
Data scientists love tools like R and Scikit-Learn, as they offer a convenient and familiar syntax for analysis tasks. However, these systems are limited to operating serially on datasets that can fit on a single node and don't allow for distributed execution. Mahout-Samsara is a linear algebra environment that offers both an easy-to-use Scala DSL and efficient distributed execution for linear algebra operations. Data scientists transitioning from R to Mahout can use the Samsara DSL for large-scale data sets with familiar R-like semantics. Machine learning and deep learning algorithms built with the Mahout-Samsara DSL are automatically parallelized and optimized to execute on distributed processing engines like Apache Spark and Apache Flink accelerated natively by CUDA, OpenCL, and OpenMP. We'll look at Mahout's distributed linear algebra capabilities and demonstrate an EigenFaces classification using Distributed SSVD executing on a GPU cluster. Machine learning practitioners will come away from this talk with a better understanding of how Samsara's linear algebra environment can help simplify developing highly scalable, CPU/GPU-accelerated machine learning and deep learning algorithms by focusing solely on the declarative specification of the algorithm without having to worry about the implementation details of a scalable distributed engine or having to learn to program with native math libraries.  Back
 
Keywords:
Accelerated Analytics, Deep Learning and AI, Algorithms and Numerical Techniques, GTC Silicon Valley 2017 - ID S7572
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Blending the Worlds of Machine Learning and Deep Learning to Make the Fastest AI Platform on GPUs
SriSatish Ambati (H2O), Arno Candel (H2O.ai)
Deep learning algorithms have benefited greatly from the recent performance gains of GPUs. However, it has been unclear whether GPUs can speed up data manipulations such as joins and aggregations and machine learning algorithms such as generalized li ...Read More
Deep learning algorithms have benefited greatly from the recent performance gains of GPUs. However, it has been unclear whether GPUs can speed up data manipulations such as joins and aggregations and machine learning algorithms such as generalized linear modeling, random forests, gradient boosting machines, and clustering. H2O.ai, the leading open source AI company, is bringing the best-of-breed data science and machine learning algorithms to GPUs, not just deep learning. In addition, H2O.ai is porting data.table to GPUs, already the fastest open-source columnar data frame library and the world's fastest implementation of the sort algorithm. This powerful combination will enable the fastest data science and machine learning pipelines for AI transformations for applications such as IoT time series, fraud prevention, anomaly detection, and many more. We'll demonstrate benchmarks for the most common algorithms relevant to enterprise AI and showcase performance gains as compared to running on CPUs.  Back
 
Keywords:
Accelerated Analytics, AI Startup, Deep Learning and AI, GTC Silicon Valley 2017 - ID S7652
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Accelerated Analytics Industry Use Cases
Renee Yao (NVIDIA)
Companies of all sizes and in all industries are driven towards digital transformation. Failure to adapt to this movement places businesses at an increased risk in current and future competitive markets. With the slow compute limitation, enterpr ...Read More

Companies of all sizes and in all industries are driven towards digital transformation. Failure to adapt to this movement places businesses at an increased risk in current and future competitive markets. With the slow compute limitation, enterprises struggle to gain valuable insights fast, monetize the data, enhance customer experience, optimize operational efficiency, and prevent fraudulent attacks all at the same time. NVIDIA helps provide deeper insights, enable dynamic correlation, and deliver predictive outcomes at superhuman speed, accuracy, and scale. We'll highlight specific accelerated analytics use cases -- powered by the NVIDIA Tesla platform, DGX-1 AI supercomputer, and NVIDIA GPU-accelerated cloud computing -- in finance, oil and gas, manufacture, retail, and telco industries.

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Keywords:
Accelerated Analytics, GTC Silicon Valley 2017 - ID S7774
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Apache Mahout's New Recommender Algorithm and Using GPUs to Speed Model Creation
Pat Ferrel (ActionML), Andy Palumbo (Cylance)
Predictive AI is often associated with product recommenders. We present a landscape of multi-domain behavioral models that predict multi-modal user preferences and behavior. This session will take the audience from first principles of the new Co ...Read More

Predictive AI is often associated with product recommenders. We present a landscape of multi-domain behavioral models that predict multi-modal user preferences and behavior. This session will take the audience from first principles of the new Correlated Cross-Occurrence (CCO) algorithms showing the important innovations that lead to new ways to predict behavior into a deep dive into as variety different use cases, for instance using dislikes to predict likes, using search terms to predict purchase, and using conversion to augment search indexes with behavioral data to produce behavioral search. Some of these are nearly impossible to address without this new technique. We show the tensor algebra that makes up the landscape. Next, we walk through the computation using real-world data. Finally, we show how Mahout's generalized CPU/GPU integration and recently added CUDA support bring significant reductions in time and cost to calculate the CCO models. We expect the audience to come away with an understanding of the kind of applications to be built CCO and how to do so in performant in cost reducing ways.  

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Keywords:
Accelerated Analytics, Algorithms and Numerical Techniques, GTC Silicon Valley 2017 - ID S7829
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Advancing Accelerated Deep Learning with IBM PowerAI
Sumit Gupta
IBM PowerAI provides the easiest on-ramp for enterprise deep learning. PowerAI helped users break deep learning training benchmarks AlexNet and VGGNet thanks to the world's only CPU-to-GPU NVIDIA NVLink interface. See how new feature develop ...Read More

IBM PowerAI provides the easiest on-ramp for enterprise deep learning. PowerAI helped users break deep learning training benchmarks AlexNet and VGGNet thanks to the world's only CPU-to-GPU NVIDIA NVLink interface. See how new feature development and performance optimizations will advance the future of deep learning in the next twelve months, including NVIDIA NVLink 2.0, leaps in distributed training, and tools that make it easier to create the next deep learning breakthrough. Learn how you can harness a faster, better and more performant experience for the future of deep learning.    

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Keywords:
Accelerated Analytics, Deep Learning and AI, GTC Silicon Valley 2017 - ID S7862
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Fastest GPU-Based OLAP and Data Mining: Big Data Analytics on DGX
Roman Raevsky (POLYMATICA)
Polymatica is an OLAP and Data Mining server with hybrid CPU+GPU architecture which turns any analytical work on billions-records data volumes into a proactive process with no waitings. Polymatica architecture uses NVIDIA Multi-GPU (i.e. in DGX- ...Read More

Polymatica is an OLAP and Data Mining server with hybrid CPU+GPU architecture which turns any analytical work on billions-records data volumes into a proactive process with no waitings. Polymatica architecture uses NVIDIA Multi-GPU (i.e. in DGX-1) in critical operations with billions of raw business data records. This allows to eliminate pauses and accelerate the speed of analytical operations for up to hundred times. You'll see the performance difference on the example of the real analytical process in retail on different hardware: 1) CPU-only calculations on 2*Intel Xeon, no GPU; 2) 2*Intel Xeon + single Tesla P100; 3) DGX-1: 2*Intel Xeon + 8*Tesla P100. Polymatica on DGX-1 become the fastest OLAP and Data Mining engine allowing advanced analytics on datasets of billions of records.

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Keywords:
Accelerated Analytics, Algorithms and Numerical Techniques, HPC and AI, GTC Europe 2017 - ID 23164
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Building Brains - Parallelisation strategies of large-scale deep learning neural networks on parallel scale out architectures like ApacheSpark using GPUs
Romeo Kienzler (IBM WATSON IOT)
New deep learning frameworks are being developed on a monthly basis. For most of them, the inventors did not have scale-out parallelisation in mind. ApacheSpark and other data parallel frameworks, on the other hand, are becoming the de-facto sta ...Read More

New deep learning frameworks are being developed on a monthly basis. For most of them, the inventors did not have scale-out parallelisation in mind. ApacheSpark and other data parallel frameworks, on the other hand, are becoming the de-facto standard for BigData analysis. In this talk, we will have a look at different deep learning frameworks and their parallelisation strategies on GPUs and ApacheSpark. Well start with DeepLearning4J and ApacheSystemML as first class citizens. We will then have a look at TensorSpark and TensorFrames and finish with CaffeOnSpark to explain concepts like Inter- and Intra-model parallelism, distributed Cross-Validation and Jeff Dean style parameter averaging.

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Keywords:
Accelerated Analytics, Algorithms and Numerical Techniques, HPC and Supercomputing, GTC Europe 2017 - ID 23201
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Distributed Deep Learning on MapR Converged Data Platform with Heterogeneous NVIDIA GPU clusters
William Cairns (MAPR TECHNOLOGIES)
We utilize a MapR converged data platform to serve as the data layer to provide distributed file system, key-value storage and streams to store and build the data pipeline. On top of that, we use Kubernetes as an orchestration layer to manage th ...Read More

We utilize a MapR converged data platform to serve as the data layer to provide distributed file system, key-value storage and streams to store and build the data pipeline. On top of that, we use Kubernetes as an orchestration layer to manage the containers to train and deploy deep learning models, as well as serve the deep learning models in the form of containers.

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Keywords:
Accelerated Analytics, Data Center and Cloud Infrastructure, Tools and Libraries, GTC Europe 2017 - ID 23223
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Neural Network for Nanoscience: Scanning Electron Microscope Image Recognition
Giuseppe Piero Brandino (EXACT LAB S.R.L.)
This session will present an overview on how we recently applied modern deep learning techniques to the wide area of nanoscience. We will focus on deep convolutional neural network training to classify Scanning Electron Microscope (SEM) images a ...Read More

This session will present an overview on how we recently applied modern deep learning techniques to the wide area of nanoscience. We will focus on deep convolutional neural network training to classify Scanning Electron Microscope (SEM) images at the nanoscale, discussing first the issues we faced, and then how we solved them by improving the standard deep learning tools. This session aims to introduce a new promising and stimulating field of research that implements deep learning techniques in the nanoscience domain, with the final aim to provide researchers with advanced and innovative tools. These will contribute to improve the scientific research in the boosting field of experimental and computational nanoscience.

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Keywords:
Accelerated Analytics, Video and Image Processing, GTC Europe 2017 - ID 23228
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Visualizing and Interrogating Black box AI Models with GPU Enabled Architecture
Zach Izham (VOLKSWAGEN), Asghar GHORBANI (VOLKSWAGEN)
In the world of analytics and AI for many, GPU-accelerated analytics is equivalent to speeding up training time. The question, however, remains is how one interprets such highly complex black box models? How these models can help decision-making ...Read More

In the world of analytics and AI for many, GPU-accelerated analytics is equivalent to speeding up training time. The question, however, remains is how one interprets such highly complex black box models? How these models can help decision-making? Well discuss and present here a GPU based architecture to not only accelerate training the models but also use the GPU based databases and visual analytics to render billions of rows to solve the challenges of interpreting these black box models. With the advent of algorithms, databases and visualization tools, all based on a GPU architecture a solution like this has become more accessible. Interactive visualization of the model, based on partial dependence analysis, is one approach to interpret these opaque models and is our focus here.

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Keywords:
Accelerated Analytics, Algorithms and Numerical Techniques, GTC Europe 2017 - ID 23244
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Scaling on One Node: Hybrid Engines With Multi-GPU on In-Memory Database Queries
Peter Strohm (JEDOX AG)
Learn how large requests on big datasets, like production or finance data, can benefit from hybrid engine approaches for calculating on in-memory databases. While hybrid architectures are state-of-the-art in specialized calculation scenarios (e. ...Read More

Learn how large requests on big datasets, like production or finance data, can benefit from hybrid engine approaches for calculating on in-memory databases. While hybrid architectures are state-of-the-art in specialized calculation scenarios (e.g., linear algebra), multi-GPU or even multicore usage in database servers is still far from everyday use. In general, the approach to handle requests on large datasets would be scaling the database resources by adding new hardware nodes to the compute cluster. We use intelligent request planning and load balancing to distribute the calculations to multi-GPU and multicore engines in one node. These calculation engines are specifically designed for handling hundreds of millions of cells in parallel with minimal merging overhead.

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Keywords:
Accelerated Analytics, Performance Optimization, HPC and AI, GTC Europe 2017 - ID 23294
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Deep Learning – Accelerating the NLP Journey
Toby LEHEUP (CREDIT SUISSE), Shahzad Chohan (CREDIT SUISSE)
Discover how Credit Suisse has implemented Deep Learning in eCommunications Surveillance, and how moving to GPU-accelerated models has yielded significant business value. The solution works on unstructured data and leverages bleeding-edge Natura ...Read More

Discover how Credit Suisse has implemented Deep Learning in eCommunications Surveillance, and how moving to GPU-accelerated models has yielded significant business value. The solution works on unstructured data and leverages bleeding-edge Natural Language Processing techniques, and will be enhanced with emotion analysis running on GPU-farms.  This talk will include a demo of the functionality.    

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Keywords:
Accelerated Analytics, Tools and Libraries, Intelligent Machines and IoT, GTC Europe 2017 - ID 23259
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Reinforcement Learning for Railway Scheduling: Overcoming Data Sparseness through Simulations
Erik NYGREN (SBB)
Deep learning optimization in real world applications is often limited by the lack of valuable data, either due to missing labels or the sparseness of relevant events (e.g. failures, anomalies) in the dataset. We face this problem when we optimi ...Read More

Deep learning optimization in real world applications is often limited by the lack of valuable data, either due to missing labels or the sparseness of relevant events (e.g. failures, anomalies) in the dataset. We face this problem when we optimize dispatching and rerouting decisions in the Swiss railway network, where the recorded data is variable over time and only contains a few valuable events. To overcome this deficiency we use the high computational power of modern GPUs to simulate millions of physically plausible scenarios. We use this artificial data to train our deep reinforcement learning algorithms to find and evaluate novel and optimal dispatching and rerouting strategies.

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Keywords:
Accelerated Analytics, Other, HPC and AI, GTC Europe 2017 - ID 23163
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Brain Research: A Pathfinder for Future HPC
Dirk Pleiter (FORSCHUNGSZENTRUM JUELICH / JUELICH SUPERCOMPUTING CENTRE)
A key driver for pushing high-performance computing is the enablement of new research. One of the biggest and most exiting scientific challenge requiring high-performance computing is to decode the human brain. Many of the research topics in thi ...Read More

A key driver for pushing high-performance computing is the enablement of new research. One of the biggest and most exiting scientific challenge requiring high-performance computing is to decode the human brain. Many of the research topics in this field require scalable compute resources or the use of advance data analytics methods (including deep learning) for processing extreme scale data volumes. GPUs are a key enabling technology and we will thus focus on the opportunities for using these for computing, data analytics and visualisation. GPU-accelerated servers based on POWER processors are here of particular interest due to the tight integration of CPU and GPU using NVLink and the enhanced data transport capabilities.

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Keywords:
Accelerated Analytics, HPC and AI, HPC and Supercomputing, GTC Europe 2017 - ID 23189
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Optimizing, Profiling, and Deploying TensorFlow AI Models in Production with GPUs
Chris Fregly (PIPELINEAI)
Using the latest advancements from TensorFlow including the Accelerated Linear Algebra (XLA) Framework, JITundefinedAOT Compiler, and Graph Transform Tool , Ill demonstrate how to optimize, profile, and deploy TensorFlow Models in GPU-based prod ...Read More

Using the latest advancements from TensorFlow including the Accelerated Linear Algebra (XLA) Framework, JITundefinedAOT Compiler, and Graph Transform Tool , Ill demonstrate how to optimize, profile, and deploy TensorFlow Models in GPU-based production environment. This talk is 100% demo based with open source tools and completely reproducible through Docker on your own GPU cluster. In addition, I spin up a GPU cloud instance for every attendee in the audience. We go through the notebooks together as I demonstrate the process of continuously training, optimizing, deploying, and serving a TensorFlow model on a large, distributed cluster of Nvidia GPUs managed by the attendees.

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Keywords:
Accelerated Analytics, Performance Optimization, Tools and Libraries, GTC Europe 2017 - ID 23363
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DGX Systems: Best Practices for Deep Learning from Desk to Data Center
Markus WEBER (NVIDIA), Haiduong VO (NVIDIA)
NVIDIA DGX Systems powered by Volta deliver breakthrough performance for today''s most popular deep learning frameworks. Attend this session to hear from DGX product experts and gain insights that will help researchers, developers, and d ...Read More

NVIDIA DGX Systems powered by Volta deliver breakthrough performance for today''s most popular deep learning frameworks. Attend this session to hear from DGX product experts and gain insights that will help researchers, developers, and data science practitioners accelerate training and iterate faster than ever. Learn (1) best practices for deploying an end-to-end deep learning practice, (2) how the newest DGX systems including DGX Station address the bottlenecks impacting your data science, and (3) how DGX software including optimized deep learning frameworks give your environment a performance advantage over GPU hardware alone.

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Keywords:
Accelerated Analytics, Computer Vision and Machine Vision, HPC and AI, GTC Europe 2017 - ID 23370
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Caffe2: A New Lightweight, Modular, and Scalable Deep Learning Framework
Marat Dukhan (FACEBOOK), Alexander SIDOROV (FACEBOOK)
Caffe2 is a lightweight, modular, and scalable deep learning framework refactored from the previous Caffe. Caffe2 has been widely used at Facebook to enable new AI & AR experiences. This talk will be divided into two parts. In the first part ...Read More

Caffe2 is a lightweight, modular, and scalable deep learning framework refactored from the previous Caffe. Caffe2 has been widely used at Facebook to enable new AI & AR experiences. This talk will be divided into two parts. In the first part, we will explain some framework basics, the strengths of Caffe2, large scale training support and will walk you through several product use-cases at Facebook including computer vision, machine translation, speech recognition and content ranking. The second part will explain how users benefit from Caffe2''s built-in neural network model compression, fast convolution for mobile CPUs, and GPU acceleration.

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Keywords:
Accelerated Analytics, Performance Optimization, Tools and Libraries, GTC Europe 2017 - ID 23450
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Writing Graph Primitives with Gunrock
Muhammad Osama (University of California Davis)
Learn how to use Gunrock, a state-of-the-art CUDA-based graph-processing library specifically designed for the GPU, to develop fast, efficient, and complex graph primitives. Gunrock achieves a balance between performance and expressiveness by couplin ...Read More
Learn how to use Gunrock, a state-of-the-art CUDA-based graph-processing library specifically designed for the GPU, to develop fast, efficient, and complex graph primitives. Gunrock achieves a balance between performance and expressiveness by coupling high-performance GPU computing primitives and optimization strategies with a high-level programming model that allows programmers to quickly develop new graph primitives with small code size and minimal GPU programming knowledge. Gunrock is a stable, powerful, and forward-looking substrate for GPU-based, graph-centric research and development. Like many graph frameworks, it leverages a bulk-synchronous programming model and targets iterative convergent graph computations. We believe that Gunrock offers both the best performance on GPU graph analytics as well as the widest range of primitives.  Back
 
Keywords:
Accelerated Analytics, Tools and Libraries, HPC and AI, GTC Silicon Valley 2018 - ID S8586
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The Need for Speed: How the Auto Industry Accelerates Machine Learning with Visual Analytics
Asghar Ghorbani (Volkswagen AG), Zach Izham (Volkswagen), Aaron Williams (MapD)
While GPU-accelerated analytics have already radically accelerated the speed of training machine learning models, data scientists and analysts still grapple with deriving insights from these complex models to better inform decision-making. The key: V ...Read More
While GPU-accelerated analytics have already radically accelerated the speed of training machine learning models, data scientists and analysts still grapple with deriving insights from these complex models to better inform decision-making. The key: Visualizing and interrogating black box models with a GPU-enabled architecture. Volkswagen and MapD will discuss how interactive, visual analytics are helping the automotive brand interactively explore the output of their ML models to interrogate them in real time, for greater accuracy and reduced biases. They'll also examine how applying the GPU Data Frame to their efforts has enabled them to accelerate data science by minimizing data transfers and made it possible for their complex, multi-platform machine learning workflows to run entirely on GPUs.  Back
 
Keywords:
Accelerated Analytics, NVIDIA Inception Program, Deep Learning and AI Frameworks, GTC Silicon Valley 2018 - ID S8468
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Fast and Scalable Subgraph Isomorphism Using Dynamic Graph Techniques
James Fox (Georgia Institute of Technology)
Finding well-connected subgraphs is a common graph analysis goal. However, well-known formulations such as the k-Clique are computationally intractable and often too restrictive. The k-Truss of a graph is defined in terms of minimal triangle counts a ...Read More
Finding well-connected subgraphs is a common graph analysis goal. However, well-known formulations such as the k-Clique are computationally intractable and often too restrictive. The k-Truss of a graph is defined in terms of minimal triangle counts and is computationally tractable to find. We'll present our novel algorithm and scalable implementation for finding the k-Truss of a graph, which uses dynamic triangle counting techniques and leverages a dynamic graph data structure and framework for the GPU. Our approach won an Innovation Award for HPEC'17 GraphChallenge, and performs anywhere from 100x to 10,000x faster than baseline benchmarks.  Back
 
Keywords:
Accelerated Analytics, Algorithms and Numerical Techniques, GTC Silicon Valley 2018 - ID S8198
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Blazing Fast SQL Analytics on Your Data Lake
Rodrigo Aramburu (BlazingDB), William Malpica (BlazingDB)
Extract analytical value out of your enterprise data lake with a state-of-the-art GPU SQL analytics engine. As businesses continue to consolidate massive datasets into data lake technologies (HDFS, AWS S3, Azure Blob, etc.), they find themselves unab ...Read More
Extract analytical value out of your enterprise data lake with a state-of-the-art GPU SQL analytics engine. As businesses continue to consolidate massive datasets into data lake technologies (HDFS, AWS S3, Azure Blob, etc.), they find themselves unable to fully leverage the value these lakes hold. Data engineering departments need to produce unique, costly ETL processes for every dataset and every tool which hopes to interact with said dataset. At BlazingDB we've built an analytics engine that runs SQL directly on open source file formats inside data lakes, currently BlazingDB's Simpatico and Apache Parquet. These file formats can be easily accessed from a variety of different tools, limit duplication of large volumes of data, and support improved data governance. Learn strong practices for ensuring your data lake doesn't turn into a swamp and how to extract the full value of your data lake investment.  Back
 
Keywords:
Accelerated Analytics, Telecom Industry Solutions, NVIDIA Inception Program, Finance, GTC Silicon Valley 2018 - ID S8484
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Building an Enterprise Machine Learning Center of Excellence
Zachary Hanif (Capital One)
Algorithmic advancements and new research capabilities frequently overshadow the infrastructure that enables that research and serves it to customers in production applications. Having a solid infrastructure for real world machine learning often ends ...Read More
Algorithmic advancements and new research capabilities frequently overshadow the infrastructure that enables that research and serves it to customers in production applications. Having a solid infrastructure for real world machine learning often ends up being the biggest determinant of success and is an exciting area of research and engineering in its own right. These environments are what allow brilliant algorithms to deliver value at scale. We'll detail how Capital One has designed its GPU computing environment to accelerate machine learning efforts and outline the services used, the framework to leverage those services, and the engineering practices used to develop and deploy well-governed, accurate models to high-volume production environments. Beyond production deployments, we'll discuss how this infrastructure performs large-scale testing of models and frameworks to explore the interactions of deep learning tools like MXNet and TensorFlow. We'll also discuss the practices that enabled Capital One to hire a high-performing team in this incredibly desirable field.  Back
 
Keywords:
Accelerated Analytics, Tools and Libraries, Data Center and Cloud Infrastructure, Finance, GTC Silicon Valley 2018 - ID S8843
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Accelerating Graph Algorithms for Government and Industry
Mahantesh Halappanavar (Pacific Northwest National Laboratory), Antonino Tumeo (Pacific Northwest National Laboratory)
We'll discuss our efforts regarding the acceleration of large-scale graph algorithms in the context of projects funded by various government agencies. Graph methods are key kernels for large-scale data analytics, as well as for several exascale appl ...Read More
We'll discuss our efforts regarding the acceleration of large-scale graph algorithms in the context of projects funded by various government agencies. Graph methods are key kernels for large-scale data analytics, as well as for several exascale application domains, including smart grids, computational biology, computational chemistry, and climate science. We'll present our latest results on distributed implementations employing GPUs and accelerators of graph kernels, such as community detection and B-matching, showing how we can tackle large-scale problems with heterogeneous supercomputers. On the basis of the experience and results in optimizing these algorithms for high performance computing platforms, we'll then discuss new requirements, upcoming opportunities, and potential solution for next-generation, high-performance, integrated graph toolkits.  Back
 
Keywords:
Accelerated Analytics, HPC and Supercomputing, GTC Silicon Valley 2018 - ID S8476
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GOAI One Year Later
Joshua Patterson (NVIDIA)
This talk will discuss the evolution of the GPU Open Analytics Initiative (GoAi) from its inception to today. GoAi, at its core, is a collection of libraries, frameworks, and APIs that lower the barrier of GPU adoption for data scientists. The goal o ...Read More
This talk will discuss the evolution of the GPU Open Analytics Initiative (GoAi) from its inception to today. GoAi, at its core, is a collection of libraries, frameworks, and APIs that lower the barrier of GPU adoption for data scientists. The goal of GoAi is to enable end to end data science workflows across many multi-GPU servers, to analyze and understand data more efficiently than ever before. To date, GoAi includes methods for performing SQL, machine learning, data processing or feature engineering, graph analytics, and graph visualization all on the GPU. This talk will discuss the who, what, when, where, and whys of GoAi, and its integration into the traditional big data world through leading open source projects like Apache Arrow and Apache Parquet. Finally, this talk will highlight major achievements of GoAi, our plans for the future, and how developers can become a part of this rapidly evolving ecosystem.  Back
 
Keywords:
Accelerated Analytics, Telecom Industry Solutions, Deep Learning and AI Frameworks, GTC Silicon Valley 2018 - ID S8502
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Efficient Data Loading for Streaming Data Processing on GPUs
Vassili Gorshkov (FastData.IO)
We'll provide an overview of a GPU-based streaming processing engine and the specific challenges and opportunities that this presents. Unlike talks on database engines, we'll focus on data loading problems. We'll first cover hardware constraints a ...Read More
We'll provide an overview of a GPU-based streaming processing engine and the specific challenges and opportunities that this presents. Unlike talks on database engines, we'll focus on data loading problems. We'll first cover hardware constraints and then move on to computational parts required for every data batch. This part includes converters to internal columnar format and string dictionary construction. We'll also cover in detail the maintenance of string dictionary for streaming data.  Back
 
Keywords:
Accelerated Analytics, Performance Optimization, NVIDIA Inception Program, GTC Silicon Valley 2018 - ID S8877
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Capitalize on Next Generation In-Memory HPC with HPE Superdome Flex (Presented by Hewlett Packard Enterprise)
Bill Dunmire (HPE)
Learn how the breakthrough HPE Superdome Flex platform equips scientists, engineers, and business lines with in-memory computing at unparalleled scale to solve complex, data-intensive problems holistically, accelerate analytics, and coupled with Nvid ...Read More
Learn how the breakthrough HPE Superdome Flex platform equips scientists, engineers, and business lines with in-memory computing at unparalleled scale to solve complex, data-intensive problems holistically, accelerate analytics, and coupled with Nvidia GPU technology, leverage large-scale data visualization to speed time to discovery and innovation.  Back
 
Keywords:
Accelerated Analytics, Computational Fluid Dynamics, Computer Aided Engineering, HPC and AI, GTC Silicon Valley 2018 - ID S8973
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Reinventing Real-Time Multidimensional Analytics Powered by GPU
Roman Raevsky (Polymatica)
We'll provide answers and business cases for three main questions: First, what were the main problems of big data analytics and how was it solved with GPUs? Second, how can you quickly analyze data and get maximum profit from it? Third, what is the ...Read More
We'll provide answers and business cases for three main questions: First, what were the main problems of big data analytics and how was it solved with GPUs? Second, how can you quickly analyze data and get maximum profit from it? Third, what is the future of business intelligence (BI)? We'll discuss the new way of analytics a unique BI solution powered by GPU, which provides real-time multidimensional analytics for all kinds of businesses. The online analytical processing and data mining server with hybrid CPU and GPU architecture gives users freedom of analytics with no pre-aggregates, and provides the fastest analytical tool for enterprise-sized raw data volumes. We'll show the results of the latest tests of analytical platform operations on different hardware, which proves the efficiency of work on GPUs. One example of user cases that we'll show is how companies around the world use this solution to analyze billions of raw business data records, and to optimize and automate their business. We'll also show the future of BI how the analytical platforms will look in the nearest future, and how the world of big data will change.  Back
 
Keywords:
Accelerated Analytics, Predictive Analytics for Retail, NVIDIA Inception Program, Finance, GTC Silicon Valley 2018 - ID S8533
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Speed at Scale: Using GPUs to Accelerate Analytics for Extreme Use Cases (Presented by MapD)
Todd Mostak (MapD)
It is common knowledge that GPUs can dramatically accelerate HPC and machine learning/AI workloads, but can they do the same for general purpose analytics? In this talk, Todd Mostak, CEO of MapD, will provide real-world examples of how a new generati ...Read More
It is common knowledge that GPUs can dramatically accelerate HPC and machine learning/AI workloads, but can they do the same for general purpose analytics? In this talk, Todd Mostak, CEO of MapD, will provide real-world examples of how a new generation of GPU-powered analytics platforms can enable enterprises from a range of verticals to dramatically accelerate the process of insight generation at scale. In particular, he will focus on how the key technical differentiators of GPUs: their massive computational bandwidth, fast memory, and native rendering pipeline, make them uniquely suited to allow analysts and data scientists to query, visualize and power machine learning over large, often high-velocity, datasets. Using the open source MapD analytics platform as an example, Todd will detail the technical approaches his team took to leverage the full parallelism of GPUs and demo how the platform allows analysts to interactively explore datasets containing tens of billions of records.  Back
 
Keywords:
Accelerated Analytics, NVIDIA Inception Program, GIS, GTC Silicon Valley 2018 - ID S81008
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Eliminating Manual Data Labeling with AI-powered Data Curation (Presented by Pure Storage)
Ben Taylor (Ziff.ai), Emily Watkins (Pure Storage)
Learn from real-world case studies where large corpora of unstructured data were indexed and organized by deep-learning pipelines. Organizations are capturing and saving exponentially more unstructured data. As a tactic to organize this data, many te ...Read More
Learn from real-world case studies where large corpora of unstructured data were indexed and organized by deep-learning pipelines. Organizations are capturing and saving exponentially more unstructured data. As a tactic to organize this data, many teams turn to manual data classification, but that human-in-the-loop process can be cost prohibitive and introduce metadata inaccuracies. By applying deep learning and cluster-based labeling, we can index petabyte-scale datasets and rapidly organize unstructured data for downstream model building and analysis. This session will teach you how to quickly switch to training on all the contents of your data lake, rather than just a subset. We will use cases studies with real-world datasets to walk through best practices for a deep learning indexing pipeline.  Back
 
Keywords:
Accelerated Analytics, Data Center and Cloud Infrastructure, GTC Silicon Valley 2018 - ID S8962
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Breaking the Speed of Interconnect with Compression for Database Applications
Felipe Aramburu (Blazing DB), Nikolay Sakharnykh (NVIDIA)
Learn strategies for efficiently employing various cascaded compression algorithms on the GPU. Many database input fields are amenable to compression since they have repeating or gradually increasing pattern, such as dates and quantities. Fast implem ...Read More
Learn strategies for efficiently employing various cascaded compression algorithms on the GPU. Many database input fields are amenable to compression since they have repeating or gradually increasing pattern, such as dates and quantities. Fast implementations of decompression algorithms such as RLE-Delta will be presented. By utilizing compression, we can achieve 10 times greater effective read bandwidth than the interconnect allows for raw data transfers. However, I/O bottlenecks still play a big role in the overall performance and data has to be moved efficiently in and out of the GPU to ensure optimal decompression rate. After a deep dive into the implementation, we'll show a real-world example of how BlazingDB leverages these compression strategies to accelerate database operations.  Back
 
Keywords:
Accelerated Analytics, NVIDIA Inception Program, Algorithms and Numerical Techniques, GTC Silicon Valley 2018 - ID S8417
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Evaluation of Hybrid Cache-Coherent Concurrent Hash Table on POWER9 System with NVLink 2
Rajesh Bordawekar (IBM T. J. Watson Research Center), Pidad Gasfar D'Souza (IBM Systems Development Lab)
At the 2014 GTC, we described a novel concurrent cache-aware hash table that used a multi-level bounded linear probing hashing algorithm. This year we'll discus how the design has expanded using a hybrid (CPU-GPU based) hash table where the data is ...Read More
At the 2014 GTC, we described a novel concurrent cache-aware hash table that used a multi-level bounded linear probing hashing algorithm. This year we'll discus how the design has expanded using a hybrid (CPU-GPU based) hash table where the data is stored on the host CPU memory and accessed via the GPU using the unified memory constructs. The hash table is designed such that multiple CPU threads can update it concurrently and multiple GPU threads can fetch data from the hash table in a cache-coherent manner using NVLink 2.0. The hash-table is implemented on a POWER9 system with NVLink 2.0 connected Tesla V100 GPUs. We'll present detailed performance measurements of throughput and virtual memory activities from CPU updates and GPU fetches. We also compare the performance of our design against a hybrid hash table built using the Cuckoo hashing approach.  Back
 
Keywords:
Accelerated Analytics, GTC Silicon Valley 2018 - ID S8172
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Graph-Centric AI for Cybersecurity
Howie Huang (The George Washington University)
Large enterprise networks and computer systems face the daily challenge of cyberattacks, which originate from software and hardware vulnerabilities and result in data theft, service interruption, and monetary loss. To address this challenge, we've d ...Read More
Large enterprise networks and computer systems face the daily challenge of cyberattacks, which originate from software and hardware vulnerabilities and result in data theft, service interruption, and monetary loss. To address this challenge, we've developed a set of graph-based machine learning techniques for accelerating threat detection on GPUs. We'll present our research on graph-centric AI that can be used to discover malicious actions in time to prevent irreversible damage to the systems. In the era of big data, these techniques help us to have a deep understanding of critical relationships in computer systems, social networks, and IoT, which is essential in many industry segments, including defense, software, finance, e-commerce, and healthcare.  Back
 
Keywords:
Accelerated Analytics, Deep Learning and AI Frameworks, Cyber Security, GTC Silicon Valley 2018 - ID S8158
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Improving the Brick and Mortar Retail Customer Experience with GPUs
Trung Tran (Clarcepto Inc)
There is a clear opportunity for retailers to generate loyalty and increase sales by focusing on the overall customer experience. We'll describe how we are developing solutions to track customer activity and build profiles based on physical store ac ...Read More
There is a clear opportunity for retailers to generate loyalty and increase sales by focusing on the overall customer experience. We'll describe how we are developing solutions to track customer activity and build profiles based on physical store activity to personalize the in-store shopping experience. We'll also describe how GPUs and deep learning are used to create these capabilities ? all while protecting personal information and privacy.  Back
 
Keywords:
Accelerated Analytics, Intelligent Video Analytics and Smart Cities, Data Center and Cloud Infrastructure, Consumer Engagement and Personalization, Computer Vision, GTC Silicon Valley 2018 - ID S8144
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Delivering an Extreme Data Analytics API for a Customer 360 View Using the Power of GPUs
Dipti Borkar (Kinetica)
We''ll discuss how Kinetica''s technology leverages the power of GPUs to deliver next-generation analytics. Also presented is a real-world use case of how the Lippo Group, one of the largest business conglomerates in Indonesia, was able to integrate ...Read More
We''ll discuss how Kinetica''s technology leverages the power of GPUs to deliver next-generation analytics. Also presented is a real-world use case of how the Lippo Group, one of the largest business conglomerates in Indonesia, was able to integrate data from multiple lines of business across several industries into a single big data analytics platform featuring an API layer with sub-second latency. We''ll discuss how their "deep and fast analytics" approach is opening up new opportunities for improved customer engagement within the business ecosystem.  Back
 
Keywords:
Accelerated Analytics, NVIDIA Inception Program, Cyber Security, Cyber Security, GTC Silicon Valley 2018 - ID S8905
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Feeding the Big Data Engine: How to Import Data in Parallel
Brian Kennedy (Simantex)
Explore new techniques in transforming traditional sequential data import and validation routines into high-speed parallel algorithms. We''ll explore some of the innovative approaches required to import and validate massive data files, using a CSV fo ...Read More
Explore new techniques in transforming traditional sequential data import and validation routines into high-speed parallel algorithms. We''ll explore some of the innovative approaches required to import and validate massive data files, using a CSV format, in parallel. We''ll discuss the challenges of designing a set of parallel algorithms to simultaneously import millions of rows of data, while honoring all of the capabilities of the CSV format including: varying column lengths between columns and across rows, quoted columns, embedded token separators, and malformed data rows. We''ll also show how we support column character count validation with the ability to mix single and multi-byte characters within a field, along with our approaches and special optimizations to allow the GPU to efficiently handle string processing. Finally, we''ll review our performance gains compared to current sequential approaches, showing that we have increased throughput by over 18,000 percent on a single GPU card, and how this can be further scaled to support multiple GPUs.  Back
 
Keywords:
Accelerated Analytics, Algorithms and Numerical Techniques, GTC Silicon Valley 2018 - ID S8443
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Flexible and Fast Machine Learning and Deep Learning with Alluxio
Yupeng Fu (Alluxio), Michael Wendt (NVIDIA)
With the exponentially-growing deluge of data today, data lakes are pooling everywhere. So, how can you harness them for critical insights and is there an easy way to tap into the multitude of different storage systems that they''re stored in? Enter ...Read More
With the exponentially-growing deluge of data today, data lakes are pooling everywhere. So, how can you harness them for critical insights and is there an easy way to tap into the multitude of different storage systems that they''re stored in? Enter Alluxio, an agnostic and fast storage abstraction, which, when paired with deep learning and GPU-accelerated analytics yields a quick and easy way to harness the data. Join NVIDIA''s Applied Solutions Engineering (ASE) team as they walk through how to use Alluxio for fun and profit.  Back
 
Keywords:
Accelerated Analytics, GTC Silicon Valley 2018 - ID S8569
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GPU-Accelerated Semantic Similarity Search at Scale
Kubilay Atasu (IBM Research)
Learn how to compute a high-quality approximation of the state-of-the-art text-similarity measure "Word Mover''s Distance" on massive datasets using a novel algorithm developed at IBM Research - Zurich. Our algorithm has linear time complex ...Read More
Learn how to compute a high-quality approximation of the state-of-the-art text-similarity measure "Word Mover''s Distance" on massive datasets using a novel algorithm developed at IBM Research - Zurich. Our algorithm has linear time complexity, requires a limited amount of working memory, and maps well into standard dense and sparse linear algebra routines. Therefore, it is very suitable for GPU acceleration! In addition, the algorithm is data parallel and exhibits a perfect weak scaling or strong scaling behavior when distributed across several GPUs. In practice, our algorithm renders the high-quality semantic-search results offered by Word Mover''s Distance applicable to massive datasets. We''ll also demonstrate applications of our algorithm in clustering, classification, and querying of news entries that are collected in real time from various data sources.  Back
 
Keywords:
Accelerated Analytics, Speech and Language Processing, GTC Silicon Valley 2018 - ID S8418
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How to Get the Most out of GPU Accelerated Database Operators
Tim Kaldewey (NVIDIA), Jiri Kraus (NVIDIA), Nikolay Sakharnykh (NVIDIA)
Early on, memory bandwidths, more than an order of magnitude higher than conventional processors have made GPUs an attractive platform for data-intensive applications. While there are many success stories about GPU-accelerated databases built from sc ...Read More
Early on, memory bandwidths, more than an order of magnitude higher than conventional processors have made GPUs an attractive platform for data-intensive applications. While there are many success stories about GPU-accelerated databases built from scratch, GPU-accelerated operations for large-scale, general-purpose databases are rather an exception than the norm. We characterize fundamental database operators like scan, filter, join, and group-by based on their memory access patterns. From these characteristics, we derive their potential for GPU acceleration, such as upper bounds for performance on current and future architectures. Starting from basic GPU implementations, we deep dive into aspects like optimizing data transfers, access, and layout, etc.  Back
 
Keywords:
Accelerated Analytics, Performance Optimization, GTC Silicon Valley 2018 - ID S8289
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BigQuery and TensorFlow: Data Warehouse + Machine Learning Enables the "Smart" Query
Kaz Sato (Google)
BigQuery is Google''s fully managed, petabyte-scale data warehouse. It''s user-defined function realizes "smart" queries with the power of machine learning, such as similarity search or recommendation on images or documents with feature vec ...Read More
BigQuery is Google''s fully managed, petabyte-scale data warehouse. It''s user-defined function realizes "smart" queries with the power of machine learning, such as similarity search or recommendation on images or documents with feature vectors and neural network prediction. We''ll see how TensorFlow and its GPU-accelerated training environment enables a powerful "data warehouse + machine learning" solution.  Back
 
Keywords:
Accelerated Analytics, Deep Learning and AI Frameworks, GTC Silicon Valley 2018 - ID S8115
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HornetsNest - Scalable Static and Dynamic Graph Algorithms Made Easy
Oded Green (Georgia Institute of Technology)
We''ll present HornetsNest, a framework for developing static and dynamic graph algorithms with relative ease. Through a small subset of graph primitives, which are the API for our framework, it is possible to implement parallel graph algorithms usin ...Read More
We''ll present HornetsNest, a framework for developing static and dynamic graph algorithms with relative ease. Through a small subset of graph primitives, which are the API for our framework, it is possible to implement parallel graph algorithms using a fairly small number of code lines. These graph primitives are optimized in the backend and as such programmers can focus on algorithm design rather than load-balancing, system utilization, and optimizations. Using these primitives, it''s possible to implement BFS in roughly 10 lines of code. Performance-wise, this BFS performs as well is its counterpart in the Gunrock library. More importantly, HornestsNest is the first framework to support a wide range of high-performing dynamic graph analytics, including new algorithms for dynamic triangle counting, dynamic page rank, and dynamic Katz centrality. Finally, we''ll cover the performance of numerous graph algorithms.  Back
 
Keywords:
Accelerated Analytics, Algorithms and Numerical Techniques, HPC and AI, GTC Silicon Valley 2018 - ID S8297
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Building a GPU-Focused CI Solution
Michael Wendt (NVIDIA)
As the number of GPU-accelerated applications have multiplied, the needs for better development tools and services have increased as well. Chief among such services is continuous integration (CI), which dramatically improves and speeds up the develop ...Read More
As the number of GPU-accelerated applications have multiplied, the needs for better development tools and services have increased as well. Chief among such services is continuous integration (CI), which dramatically improves and speeds up the development life cycle through automated builds and integration testing. CI for GPU-accelerated applications comes with its own set of challenges, but the rewards can be enormous. We'll walk through how we implemented CI for the NVIDIA GPU Cloud by leaning on open source solutions such as Jenkins, discuss the lessons we learned in the process, and demonstrate how other such systems should be built in the future.  Back
 
Keywords:
Accelerated Analytics, Tools and Libraries, GTC Silicon Valley 2018 - ID S8563
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Hornet: An Efficient Data Structure for Dynamic Sparse Graphs and Matrices
Oded Green (Georgia Institute of Technology)
We'll present Hornet, formerly known as cuSTINGER, a data structure designed for sparse dynamic graphs and matrices. Hornet scales to massive datasets while supporting very fast updates, over 200 million updates per second on a single Tesla P100 GPU ...Read More
We'll present Hornet, formerly known as cuSTINGER, a data structure designed for sparse dynamic graphs and matrices. Hornet scales to massive datasets while supporting very fast updates, over 200 million updates per second on a single Tesla P100 GPU. We'll show that replacing CSR, a popular data structure for sparse data, with Hornet does not change the execution time. We'll also show that the memory utilization of Hornet is within that of CSR and COO, and briefly show performance results of several analytics using Hornet. We'll cover the programming model for Hornet in a separate talk.  Back
 
Keywords:
Accelerated Analytics, Tools and Libraries, HPC and AI, GTC Silicon Valley 2018 - ID S8177
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Extending Splunk with GPUs
Keith Kraus (NVIDIA), Joshua Patterson (NVIDIA)
As cybersecurity data volumes grow, even the best designed SIEMs struggle to perform complex analytics on a large range of data with interactive speeds. We'll discuss how NVIDIA GPU accelerated its own Splunk instance with technologies that are a pa ...Read More
As cybersecurity data volumes grow, even the best designed SIEMs struggle to perform complex analytics on a large range of data with interactive speeds. We'll discuss how NVIDIA GPU accelerated its own Splunk instance with technologies that are a part of the GPU Open Analytics Initiative, GOAI, to drastically improve cyberhunting. Using tools such as Anaconda, BlazingDB, Graphistry, and MapD, NVIDIA interactively explored billions of events faster than ever to detect threats and perform root cause analysis. We'll walk through how cyberdefenders can use open source tools and libraries to accelerate their own Splunk instance, with code samples and how to's. Finally, we'll discuss how to stay involved in the GPU-accelerated Splunk community.  Back
 
Keywords:
Accelerated Analytics, Telecom Industry Solutions, Cyber Security, GTC Silicon Valley 2018 - ID S8499
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Acoustics and Audio Processing
Presentation
Media
Interactive 3D Audio Rendering Systems
Nicolas Tsingos
Learn how to leverage GPUs for interactive audio rendering. This session will give a short overview of the architecture of current GPUs, emphasizing some key differences between GPU and CPUs programming models for audio processing. We will illus ...Read More

Learn how to leverage GPUs for interactive audio rendering. This session will give a short overview of the architecture of current GPUs, emphasizing some key differences between GPU and CPUs programming models for audio processing. We will illustrate the benefits of GPU-accelerated audio rendering with results from 3D audio processing and sound scattering simulations. Finally, we will discuss best practices for GPU implementations as well as future opportunities for audio rendering on massively parallel architectures.

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Keywords:
Acoustics and Audio Processing, Rendering and Ray Tracing, Signal and Audio Processing, GTC Silicon Valley 2010 - ID 2042
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Implementing CUDA Audio Networks
Giancarlo Del Sordo
Learn how to implement a commercial software library that exploits CUDA for audio applications. We focus on the overall threading architecture and the underlying math for implementing general purpose audio processing in CUDA devices. Covers the ...Read More

Learn how to implement a commercial software library that exploits CUDA for audio applications. We focus on the overall threading architecture and the underlying math for implementing general purpose audio processing in CUDA devices. Covers the use of inter-process communication to make a plug-in implementation loadable in 32 bit hosts installed in 64 bit systems, distributing the GPU load on remote servers, and creating a CUDA network for high-end purposes such as a big recording facility.

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Keywords:
Acoustics and Audio Processing, Signal and Audio Processing, GTC Silicon Valley 2010 - ID S102076
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Real-time Multichannel Audio Convolution
Jose Antonio Belloch (PhD Student)
Learn how a synthesis of 3D sound scenes can be achieved using a peer-to-peer music streaming environment and GPU. We will discuss the technical and cost benefits to this approach, while noting that it frees the CPU for other tasks. ...Read More

Learn how a synthesis of 3D sound scenes can be achieved using a peer-to-peer music streaming environment and GPU. We will discuss the technical and cost benefits to this approach, while noting that it frees the CPU for other tasks.

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Keywords:
Acoustics and Audio Processing, Signal and Audio Processing, GTC Silicon Valley 2010 - ID S102116
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Exploring Recognition Network Representations for Efficient Speech Inference on the GPU
Jike Chong
We explore two contending recognition network representations for speech inference engines: the linear lexical model (LLM) and the weighted finite state transducer (WFST) on NVIDIA GTX285 and GTX480 GPUs. We demonstrate that while an inference e ...Read More

We explore two contending recognition network representations for speech inference engines: the linear lexical model (LLM) and the weighted finite state transducer (WFST) on NVIDIA GTX285 and GTX480 GPUs. We demonstrate that while an inference engine using the simpler LLM representation evaluates 22x more transitions per second than the advanced WFST representation, the simple structure of the LLM representation allows 4.7-6.4x faster evaluation and 53-65x faster operands gathering for each state transition. We illustrate that the performance of a speech inference engine based on the LLM representation is competitive with the WFST representation on highly parallel GPUs.

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Keywords:
Acoustics and Audio Processing, GTC Silicon Valley 2010 - ID P10C01
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Efficient Automatic Speech Recognition on the GPU
Jike Chong
Automatic speech recognition (ASR) technology is emerging as a critical component in data analytics for a wealth of media data being generated everyday. ASR-based applications contain fine-grained concurrency that has great potential to be explo ...Read More

Automatic speech recognition (ASR) technology is emerging as a critical component in data analytics for a wealth of media data being generated everyday. ASR-based applications contain fine-grained concurrency that has great potential to be exploited on the GPU. However, the state-of-art ASR algorithm involves a highly parallel graph traversal on an irregular graph with millions of states and arcs, making efficient parallel implementations highly challenging. We present four generalizable techniques including: dynamic data-gather buffer, find-unique, lock-free data structures using atomics, and hybrid global/local task queues. When used together, these techniques can effectively resolve ASR implementation challenges on an NVIDIA GPU.

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Keywords:
Acoustics and Audio Processing, GTC Silicon Valley 2010 - ID P10C02
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HYDRA - A Hybrid CPU/GPU Speech Recognition Engine for Real-Time LVCSR
Jungsuk Kim (Carnegie Mellon Silicon Valley)
HYDRA, a real-time LVCSR (Large Vocabulary Speech Recognition) engine that performs decoding on CPU, GPU or hybrid CPU/GPU platforms is presented in this talk. While prior works have demonstrated the effectiveness of manycore graphic processing ...Read More

HYDRA, a real-time LVCSR (Large Vocabulary Speech Recognition) engine that performs decoding on CPU, GPU or hybrid CPU/GPU platforms is presented in this talk. While prior works have demonstrated the effectiveness of manycore graphic processing units (GPU) for high-throughput, limited vocabulary speech recognition, they are unsuitable for recognition with large acoustic and language models due to the limited memory. To overcome this limitation, we have developed a novel architecture for speech recognition decoding that jointly leverages manycore graphic processing units (GPU) and multicore processors (CPU) to perform speech recognition even when large acoustic and language models are applied. The proposed architecture can perform speech recognition at up to 5x faster than real-time with a recognition vocabulary of more than 1 Million words.

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Keywords:
Acoustics and Audio Processing, GTC Silicon Valley 2013 - ID S3406
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Additive Manufacturing
Presentation
Media
Realizing the Future of Making Things with Generative Design
Brian Frank (Autodesk)
Autodesk Generative Design harnesses the compute power of the NVIDIA GPU to deliver a full Design-to-Make workflow for today's product designers and engineers. Learn how the future of computing will enable better performing designs to be created wit ...Read More
Autodesk Generative Design harnesses the compute power of the NVIDIA GPU to deliver a full Design-to-Make workflow for today's product designers and engineers. Learn how the future of computing will enable better performing designs to be created with less time and effort than traditional engineering approaches. Autodesk Generative Design allows the user to fully explore possible design spaces, incorporating materials and manufacturing methods into the creation of design solutions.  Back
 
Keywords:
Additive Manufacturing, GTC Silicon Valley 2018 - ID S8600
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Deep Learning at the Edge (Presented by HP Inc.)
Bruce Blaho (HP Inc.)
Come see how to do deep learning development on your desktop, with examples from 3D Printing and Geoscience.  In this session you will see how new powerful workstations are being used to create advanced deep learning solutions at the edge of the ...Read More
Come see how to do deep learning development on your desktop, with examples from 3D Printing and Geoscience.  In this session you will see how new powerful workstations are being used to create advanced deep learning solutions at the edge of the network - and why this is a strong complement to cloud-only based approaches.  We''ll share detailed, concrete examples from expert speakers: CGG is a geoscience company leading the use of deep learning to interpret terabyte-sized seismic data sets to find important underground features such as oil & gas deposits. HP''s Jet Fusion 3D Printers use complex thermal control systems to optimize part printing and material properties.  We will explore the deep learning based algorithms being developed for HP''s next generation of 3D printers.   Bruce Blaho - Fellow & Workstations Chief Technologist, HP Inc. Steve Dominguez - Team Lead Seismic Interpretation Software, CGG Dr. Jun Zeng - Principal Investigator, HP Labs Print & 3D, HP Inc. Dr. He Luan - Research Scientist, HP Labs Print & 3D, HP Inc.  Back
 
Keywords:
Additive Manufacturing, Deep Learning and AI Frameworks, GTC Silicon Valley 2018 - ID S81044
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Advanced AI Learning Techniques (incl. GANs and NTMs)
Presentation
Media
Discover Orders in Unordered Datasets: Generative Markov Networks
Yao-Hung Tsai (Carnegie Mellon University)
In this work, we argue that for any dataset even without explicit orders, there exists implicit orders/relationships for the data. Aiming at finding these orders and relationships, we introduce novel generative markov networks (GMNs) that considers a ...Read More
In this work, we argue that for any dataset even without explicit orders, there exists implicit orders/relationships for the data. Aiming at finding these orders and relationships, we introduce novel generative markov networks (GMNs) that considers a Markov Chain data generation process. To make the learning of transition operator tractable and flexible, we utilize neural networks as smooth function approximators. Moreover, we propose a batch-wise permutation training regime to ensure an ergodic training process for the Markov Chain. We'll show that GMNs are able to discover orders and relationships in datasets, and can also perform well on benchmark one-shot recognition task.  Back
 
Keywords:
Advanced AI Learning Techniques (incl. GANs and NTMs), GTC Silicon Valley 2018 - ID S8577
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Deep Learning of Severe Weather Forecast Data
David Gagne (National Center for Atmospheric Research)
Attendees will learn how deep learning models identify severe weather hazards, how deep learning severe weather diagnosis compares with other machine learning methods, and what weather features deep learning considers most important for determining w ...Read More
Attendees will learn how deep learning models identify severe weather hazards, how deep learning severe weather diagnosis compares with other machine learning methods, and what weather features deep learning considers most important for determining whether a storm will produce severe weather or not. Severe weather hazards, such as tornadoes, hail, high winds, and flash floods, cause billions of dollars in property damage and injure or kill hundreds of people in the U.S. each year. Improved forecasts of the potential for severe weather enables decision makers to take actions to save lives and property. Machine learning and deep learning models extract spatial information from observations and numerical weather prediction model output to predict the probability of severe weather based on whether or not some form of severe weather was reported by the public. Convolutional neural networks and generative adversarial networks are compared against principal component analysis encodings to determine how much skill deep learning adds over traditional methods. The deep learning models are interrogated to identify important variables and spatial features for severe weather prediction.  Back
 
Keywords:
Advanced AI Learning Techniques (incl. GANs and NTMs), Climate, Weather, Ocean Modeling, HPC and AI, GTC Silicon Valley 2018 - ID S8455
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Disrupting Logistics and Optimization with AI
Karim Beguir (InstaDeep)
In this talk, you will get a detailed yet accessible look at how AI is disrupting logistics. Firms have for years been using classical optimization algorithms to make decisions such as how to deliver goods to multiple clients in a city, place package ...Read More
In this talk, you will get a detailed yet accessible look at how AI is disrupting logistics. Firms have for years been using classical optimization algorithms to make decisions such as how to deliver goods to multiple clients in a city, place packages in a warehouse or route orders. Such algorithms are often built on heuristics which experts have designed to get reasonable solutions quickly. Recent advances in Deep Learning and Reinforcement learning are however making it possible to build AI systems that tackle these optimization problems from scratch. Through constant learning, a modern AI system can match and even beat existing optimization algorithms, or deliver faster solutions thanks to GPU parallel processing. Companies can now leverage these advances into significant efficiency gains for their operations.  Back
 
Keywords:
Advanced AI Learning Techniques (incl. GANs and NTMs), Performance Optimization, NVIDIA Inception Program, Inventory, GTC Silicon Valley 2018 - ID S8432
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The Latest of Project Apollo and Centralized in-car Computing Platform for Autonomous Driving
Xing Yuan (Baidu)
Apollo Computing Unit (ACU), a mass production-oriented autonomous driving computing platform launched by Baidu, mainly features Apollo Pilot system and Intelligent Map service. As an important part of the Apollo platform, ACU is launched for ma ...Read More

Apollo Computing Unit (ACU), a mass production-oriented autonomous driving computing platform launched by Baidu, mainly features Apollo Pilot system and Intelligent Map service. As an important part of the Apollo platform, ACU is launched for mass production by the Baidu''s partners. Based on the different computing capabilities required by different scenarios, it is divided into three series of products: ACU-Basic, ACU-Advanced, and ACU-Professional.

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Keywords:
Advanced AI Learning Techniques (incl. GANs and NTMs), Autonomous Vehicles, Algorithms and Numerical Techniques, Autonomous Driving, GTC Silicon Valley 2018 - ID S8902
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Recurrent Generative Adversarial Neural Networks for Compressive Imaging
Morteza Mardani (Stanford University)
We''ll present recurrent generative adversarial networks (GANs) for image recovery from compressed measurements, which has applications ranging from undersampled medical image reconstruction to image super-resolution. State-of-the-art analytics are n ...Read More
We''ll present recurrent generative adversarial networks (GANs) for image recovery from compressed measurements, which has applications ranging from undersampled medical image reconstruction to image super-resolution. State-of-the-art analytics are not aware of the image perceptual quality, and demand iterative algorithms that incur significant computational overhead for real-time tasks. To sidestep these hurdles, we introduce a novel compressive imaging framework using deep neural networks that approximates a low-dimensional manifold of images using GANs. To ensure the images are consistent with the measurements, a recurrent GAN architecture is deployed that consists of multiple alternative blocks of generator networks and affine projection, which is then followed by a discriminator network to score the perceptual quality of the generated images. A deep residual network with skip connections is used for the generator, while the discriminator is a multilayer Perceptron. Experiments performed with real-world contrast enhanced MRI data corroborate the superior diagnostic quality and faster reconstruction for the retrieved images relative to state-of-the-art schemes.  Back
 
Keywords:
Advanced AI Learning Techniques (incl. GANs and NTMs), Computer Vision, Medical Imaging and Radiology, GTC Silicon Valley 2018 - ID S8197
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Application of Generative Deep Neural Networks for Mass Customization of Patient-Specific Products
Sergei Azernikov (Glidewell Dental), Jyh-Jing Hwang (UC Berkeley)
We''ll show how generative adversarial networks (GANs) running on GPUs are about to revolutionize mass customization of patient-specific products at Glidewell Dental. Every day, our labs produce thousands of patient-specific items, such as dental res ...Read More
We''ll show how generative adversarial networks (GANs) running on GPUs are about to revolutionize mass customization of patient-specific products at Glidewell Dental. Every day, our labs produce thousands of patient-specific items, such as dental restorations, implants, and appliances. To deliver functional and aesthetic products, high levels of precision and consistency are essential. Traditionally, dental restoration design and manufacturing process was very labor intensive and required highly skilled dental professionals. Today, with the advances in CAD/CAM, the amount of manual labor has been significantly reduced; however, there are still many aspects of the process that require human intervention due to the fact that some of these aspects are hard to formalize and therefore impossible to automate with traditional tools. The convergence of several technologies, such as deep learning, GPGPU, and cloud computing, has allowed us to effectively train generative models on historical data. These models are now capable of automatically generating high-quality patient-specific designs.  Back
 
Keywords:
Advanced AI Learning Techniques (incl. GANs and NTMs), Consumer Engagement and Personalization, GTC Silicon Valley 2018 - ID S8155
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Learning to Learn, Deep Learning for Robotics, Deep Reinforcement Learning, AI for Manufacturing and Logistics
Pieter Abbeel (UC Berkeley / OpenAI / Gradescope)
We''ll introduce the latest advances on topics such as learning-to-learn, meta-learning, deep learning for robotics, deep reinforcement learning, and AI for manufacturing and logistics. ...Read More

We''ll introduce the latest advances on topics such as learning-to-learn, meta-learning, deep learning for robotics, deep reinforcement learning, and AI for manufacturing and logistics.

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Keywords:
Advanced AI Learning Techniques (incl. GANs and NTMs), IoT, Robotics & Drones, Autonomous Machines, Robotics & Autonomous Machines, GTC Silicon Valley 2018 - ID S8118
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Learning with Opponent-Learning Awareness
Jakob Foerster (University of Oxford)
We'll discuss deep reinforcement learning in multi-agent settings, focusing on learning with opponent-learning awareness, a novel multi-agent reinforcement learning method that allows one agent to consider the learning dynamics of another agent. You ...Read More
We'll discuss deep reinforcement learning in multi-agent settings, focusing on learning with opponent-learning awareness, a novel multi-agent reinforcement learning method that allows one agent to consider the learning dynamics of another agent. You'll learn that this not only stabilizes learning in multi-agent settings, but also leads to emergence of cooperation. A key question relevant to autonomous cars is how to maintain cooperation between self-interested learning agents in a multi-agent setting.  Back
 
Keywords:
Advanced AI Learning Techniques (incl. GANs and NTMs), Algorithms and Numerical Techniques, GTC Silicon Valley 2018 - ID S8685
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Debug and Approve your Deep Networks by Overcoming the Black Box Problem
Tsvi Achler (Optimizing Mind), Peter Feghali (Optimizing Mind)
Networks may learn to perform tasks by cheating in unknown and unexpected ways, which may be a liability for the developer. Feedforward networks are the basis of artificial neural networks such as deep, convolution, recurrent, networks, and even ...Read More
Networks may learn to perform tasks by cheating in unknown and unexpected ways, which may be a liability for the developer. Feedforward networks are the basis of artificial neural networks such as deep, convolution, recurrent, networks, and even simpler regression methods. However the internal decision processes of Feedforward networks are difficult to explain: they are known to be a "black-box". This is especially problematic in applications where consequences of an error can be severe, such as in medicine, banking, or self-driving cars. Optimizing Mind has developed a new type of feedback neural networks motivated by neuroscience that allows easier understanding of the internal decision process. Developers, regulators, and users can better understand their AI, reduce unexpected surprises, and liability by having Feedforward networks converted to our Illuminated form to explain the internal decision processes. We'll demonstrate some of these benefits.  Back
 
Keywords:
Advanced AI Learning Techniques (incl. GANs and NTMs), NVIDIA Inception Program, Deep Learning and AI Frameworks, GTC Silicon Valley 2018 - ID S8554
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Advanced Driver Assistance Systems (ADAS)
Presentation
Media
Real-time Traffic Sign Recognition on Mobile Processors
Victor Eruhimov (Itseez, Inc.)
There is a growing need for fast and power-efficient computer vision on embedded devices. This session will focus on computer vision capabilities on embedded platforms available to ADAS developers, covering OpenCV CUDA implementation and the new ...Read More

There is a growing need for fast and power-efficient computer vision on embedded devices. This session will focus on computer vision capabilities on embedded platforms available to ADAS developers, covering OpenCV CUDA implementation and the new computer vision standard OpenVX. In addition, Itseez traffic sign detection will be showcased. The algorithm is capable of detecting speed limit signs for both North America and EMEA regions as well as several other signs, delivering faster than real-time performance on an embedded platform with a mobile grade GPU.

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Keywords:
Advanced Driver Assistance Systems (ADAS), Automotive, Computer Vision, GTC Silicon Valley 2013 - ID S3548
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Virtualization of Tegra in Automotive Applications: Integration of Head-Unit and Instrument Cluster
Stefaan Sonck Thiebaut (OpenSynergy)
This talk will introduce the main challenges in the next generation of automotive infotainment applications: OEMs want to take advantage of open source solutions like Linux and Android yet have very high requirements on safety, security and boot ...Read More

This talk will introduce the main challenges in the next generation of automotive infotainment applications: OEMs want to take advantage of open source solutions like Linux and Android yet have very high requirements on safety, security and boot-times. In addition, to reduce costs, more functionality needs to be integrated on a single processor. An example of this is the integration of the head-unit and the instrument cluster as two displays of a single device. As a solution to these requirements, we describe a software architecture that uses virtualization with a micro-kernel and that is already implemented and available on NVIDIA Tegra3. We will give a brief outlook on the next steps regarding the sharing of the GPU and hardware virtualization.

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Keywords:
Advanced Driver Assistance Systems (ADAS), Automotive, In-Vehicle Infotainment (IVI) & Safety, Instrument Clusters & Heads-Up Display (HUD), GTC Silicon Valley 2013 - ID S3577
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Aerospace and Defense
Presentation
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Big Geospatial Data + Deep Learning + High Performance Computing = Geospatial Intelligence
Bingcai Zhang (BAE Systems)
We present two algorithms that are specifically designed to accurately detect geospatial objects in geospatial images. Combining these two algorithms with deep learning algorithms, we have achieved detection accuracy over 99% for vehicles, positional ...Read More
We present two algorithms that are specifically designed to accurately detect geospatial objects in geospatial images. Combining these two algorithms with deep learning algorithms, we have achieved detection accuracy over 99% for vehicles, positional accuracy of within 6 pixels, orientation accuracy of less than 10 degrees, and false positive error rate of 0.001% with 7.5cm GSD aerial images. In essence, our algorithms induce learning capability from deep learning into template image matching in geospatial intelligence. Our algorithms reduce false positive error rate by an order of magnitude over softmax classifier. With over 99% accuracy, we believe this may be the game changer in geospatial intelligence domain.  Back
 
Keywords:
Aerospace and Defense, Big Data Analytics, Deep Learning and AI, GTC Silicon Valley 2016 - ID S6260
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GPU-Accelerated Graph Query for Cyber Applications
Jim Carbonaro (Blazegraph)
Cyberspace is a critical domain for government and commercial organizations. It is about networks, devices, and how they interact. Graphs model nodes and links and how they are connected. Defending the critical networks in cyberspace requires process ...Read More
Cyberspace is a critical domain for government and commercial organizations. It is about networks, devices, and how they interact. Graphs model nodes and links and how they are connected. Defending the critical networks in cyberspace requires processing and analyzing extremely large quantities of graph data in near-real time. Key cyber analytics and data sets ranging from Topological Vulnerability Analysis, Traffic Flow Analysis, and Network Attack Graphs are graphs. This session will discuss how Blazegraph GPU meets this challenge by delivering near-real time performance at a very large data scales, uses a flexible and updatable graph representation to support complex analytics, and supports existing graph frameworks (RDF, Tinkerpop) and query languages (SPARQL).  Back
 
Keywords:
Aerospace and Defense, Big Data Analytics, Deep Learning and AI, GTC Silicon Valley 2016 - ID S6337
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Agile Condor: Scalable High Performance Embedded Computing Architecture
Mark Barnell (Air Force Research Laboratory), Christopher Capraro (SRC)
The Air Force Research Laboratory Information Directorate Advanced Computing and Communications Division is developing a new computing architecture using GPUs, designed to provide high-performance embedded computing (HPEC) pod solution to meet operat ...Read More
The Air Force Research Laboratory Information Directorate Advanced Computing and Communications Division is developing a new computing architecture using GPUs, designed to provide high-performance embedded computing (HPEC) pod solution to meet operational and tactical real-time processing intelligence surveillance and reconnaissance (ISR) missions. This newly designed system, Agile Condor, is a scalable and HPEC system and based on open industry standards that will increase, far beyond the current state of the art, computational capability within the restrictive size, weight, and power constraints of unmanned aircraft systems' external "pod" payloads.  Back
 
Keywords:
Aerospace and Defense, Embedded, GTC Silicon Valley 2016 - ID P6292
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Algorithms and Numerical Techniques
Presentation
Media
Parallel Low Rank LU and Cholesky Refactorization
Lung-Sheng Chien (NVIDIA)
Attendees can learn how to use a low-rank update in linear solver during a nonlinear process--for example, linear programming, structural mechanics, and circuit simulation. A GPU-friendly version is proposed, which is mainly based on BLAS2 operations ...Read More
Attendees can learn how to use a low-rank update in linear solver during a nonlinear process--for example, linear programming, structural mechanics, and circuit simulation. A GPU-friendly version is proposed, which is mainly based on BLAS2 operations. Compared to traditional approaches, with BLAS2 operations, we can hide instruction latency well and achieve full bandwidth of a many-core processor. In this talk, we describe the basic idea of low-rank update and show up to 5x speedup from complexity analysis.  Back
 
Keywords:
Algorithms and Numerical Techniques, Computer-Aided Engineering, GTC Silicon Valley 2016 - ID S6129
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Optimizing Instruction-Bound Kernels in Dissipative Particle Dynamics
Yu-Hang Tang (Division of Applied Mathematics, Brown University)
In this talk, we report algorithmic and instruction-level optimizations used in uDeviceX, a CUDA particle simulator for biomedical microfluidic devices. First, an FMA-intense random number generator (RNG) was proposed by exploiting the chaotic l ...Read More

In this talk, we report algorithmic and instruction-level optimizations used in uDeviceX, a CUDA particle simulator for biomedical microfluidic devices. First, an FMA-intense random number generator (RNG) was proposed by exploiting the chaotic logistic map. This RNG can take advantage of the higher FP-to-integer instruction throughput ratio of CUDA GPUs to generate a large number of high quality random streams in situ. Second, warp-votes and shared memory were used to consolidate workload from diverging warps. Last, inline PTX was used to emulate 24-bit integer arithmetics by their floating point counterparts in order to increase throughput. An implementation using C++ templates ensures that no type-casting overhead is triggered and also guards the technique from unintentional usage.

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Keywords:
Algorithms and Numerical Techniques, Computational Biology and Chemistry, Performance Optimization, GTC Silicon Valley 2016 - ID S6140
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Effective Evaluation of Betweenness Centrality on Multi-GPU Systems
Massimo Bernaschi (National Research Council of Italy)
Learn how to use (multi) GPU and CUDA to speed up the process of ranking the importance of each node in a large scale network. You will see how to solve an extraordinary challenge, that is the exact computation of Betweenness Centrality, by using as ...Read More
Learn how to use (multi) GPU and CUDA to speed up the process of ranking the importance of each node in a large scale network. You will see how to solve an extraordinary challenge, that is the exact computation of Betweenness Centrality, by using as building blocks relatively simple algorithms, like the Breadth First Search, that have been highly tuned for latest generation GPU cards. Our approach is fully scalable and overcomes the limitation on the size of the graph that can be studied on a single GPU. We'll present results obtained on both synthetic and real-world graphs.  Back
 
Keywords:
Algorithms and Numerical Techniques, Performance Optimization, GTC Silicon Valley 2016 - ID S6157
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Parallel Methods for Verifying the Consistency of Weakly-Ordered Architectures
Adam McLaughlin (Georgia Institute of Technology)
Contemporary microprocessors use relaxed memory consistency models to allow for aggressive optimizations in hardware. This enhancement in performance comes at the cost of design complexity and verification effort. In particular, verifying an executio ...Read More
Contemporary microprocessors use relaxed memory consistency models to allow for aggressive optimizations in hardware. This enhancement in performance comes at the cost of design complexity and verification effort. In particular, verifying an execution of a program against its system's memory consistency model is an NP-complete problem. This session improves upon existing work by introducing an algorithm that not only reduces the time complexity of the verification process, but also facilitates the development of parallel algorithms for solving these problems. For large tests of interest, our GPU implementation achieves an average application speedup of 26x over existing techniques in use at NVIDIA.  Back
 
Keywords:
Algorithms and Numerical Techniques, Big Data Analytics, Tools and Libraries, GTC Silicon Valley 2016 - ID S6180
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Not Just a Universal Crutch: Other Useful Things to Do with atomicCAS
Elmar Westphal (Forschungszentrum Julich GmbH)
There is more to atomicCAS than the double-precision atomicAdd loop from the programming guide. Something different from the universal atomic operation loop it represents. We'll show how to build shared, memory-based hash function loops to solve d ...Read More
There is more to atomicCAS than the double-precision atomicAdd loop from the programming guide. Something different from the universal atomic operation loop it represents. We'll show how to build shared, memory-based hash function loops to solve different counting and grouping problems at warp- and block-level. Variations of this loop can be used to count unique elements in a block, find threads sharing common data elements, or speed up histogram building for large numbers of bins. With the now natively implemented atomic operations on shared memory on Maxwell, these functions can be significantly faster than algorithms optimised for other architectures.  Back
 
Keywords:
Algorithms and Numerical Techniques, Performance Optimization, GTC Silicon Valley 2016 - ID S6220
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Hierarchical Computations on Manycore Architectures
Hatem Ltaief (Extreme Computing Research Center, KAUST)
Learn about a new hierarchical matrix structure for fast linear algebra computations on GPUs! Recursivity, tree traversal, hierarchical data layout, and batched kernel executions are some of the ingredients of a new HPC recipe for computing challengi ...Read More
Learn about a new hierarchical matrix structure for fast linear algebra computations on GPUs! Recursivity, tree traversal, hierarchical data layout, and batched kernel executions are some of the ingredients of a new HPC recipe for computing challenging linear algebra operations and solving large scientific problems (e.g., spatial statistics) on GPUs. By exploiting the low-rank matrix representations, the original dense matrix of the problem can be approximated, which results in saving the memory footprint and reducing the algorithmic complexity, while still maintaining an adequate solution accuracy. In addition, the talk showcases a new high-performance hierarchical symmetric eigensolver and SVD, juicing the horsepower out of multiple GPUs to the fullest.  Back
 
Keywords:
Algorithms and Numerical Techniques, Performance Optimization, Tools and Libraries, GTC Silicon Valley 2016 - ID S6230
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GPU Accelerated Markov Decision Process in Crowd Simulation
Benjamin Hernandez (Oak Ridge National Laboratory), Sergio Ruiz (Technologico de Monterrey)
Markov decision processes have been used in real-world path planning, where environment information is incomplete or dynamic. The problem with the MDP formalism is that its state space grows exponentially with the number of domain variables, and its ...Read More
Markov decision processes have been used in real-world path planning, where environment information is incomplete or dynamic. The problem with the MDP formalism is that its state space grows exponentially with the number of domain variables, and its inference methods grow with the number of available actions. To overcome this issue, we formulate an MDP solver in terms of matrix multiplications, based on the value iteration algorithm; thus we can take advantage of GPUs to produce interactively obstacle-free paths in the form of an optimal policy. We'll present a performance analysis of our technique using Jetson TK1, CPU, and GPU platforms. Our algorithm presents 90x speed-up in GPUs, and 30x speed-up in the Jetson TK1 in contrast with its CPU multi-threaded version.  Back
 
Keywords:
Algorithms and Numerical Techniques, Deep Learning and AI, GTC Silicon Valley 2016 - ID S6268
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XMP Library Internals: Modular Multiplication on Kepler and Maxwell
Niall Emmart (University of Massachusetts)
We'll present an overview of the internals of the XMP multiple precision library and take a detailed look at the low-level algorithms used for modular squaring and modular multiplication on Kepler and present novel algorithms for Maxwell. Modular ...Read More
We'll present an overview of the internals of the XMP multiple precision library and take a detailed look at the low-level algorithms used for modular squaring and modular multiplication on Kepler and present novel algorithms for Maxwell. Modular multiplication is a performance-critical primitive and widely used in cryptographic algorithms from prime testing and factorization to public key/private key algorithms such as RSA, Diffie-Hellman, and digital signatures.  Back
 
Keywords:
Algorithms and Numerical Techniques, Tools and Libraries, GTC Silicon Valley 2016 - ID S6349
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Simulating a Quantum Annealer with GPU-Based Monte Carlo Algorithms
James King (D-Wave Systems)
Learn how the world's most powerful quantum computers are simulated and benchmarked using GPU-based Monte Carlo algorithms. We'll introduce D-Wave's quantum annealing platform, describe several Monte Carlo algorithms for their simulation, an ...Read More
Learn how the world's most powerful quantum computers are simulated and benchmarked using GPU-based Monte Carlo algorithms. We'll introduce D-Wave's quantum annealing platform, describe several Monte Carlo algorithms for their simulation, and compare CPU- and GPU-based implementations of these algorithms. In particular, we'll focus on considerations of memory layout and fast mathematical functions to maximize speed. Finally, we'll present benchmarking results, including CPU-based algorithms, GPU-based algorithms, and D-Wave's latest-generation quantum annealers.  Back
 
Keywords:
Algorithms and Numerical Techniques, Computational Physics, HPC and Supercomputing, GTC Silicon Valley 2016 - ID S6380
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GPU Acceleration of Cholesky's Factorization in CHOLMOD: Batching, Hybrid and Multi-GPU
Steven Rennich (NVIDIA)
Sparse matrix factorization is a fundamental tool in scientific computing and has been shown to be well accelerated using GPUs. Yet applying the full capability of the GPU to the factorization operation remains a challenge. This talk covers the lates ...Read More
Sparse matrix factorization is a fundamental tool in scientific computing and has been shown to be well accelerated using GPUs. Yet applying the full capability of the GPU to the factorization operation remains a challenge. This talk covers the latest GPU optimizations that have been applied to the Cholesky factorization algorithm within the well-known SuiteSparse/CHOLMOD linear solver. These optimizations include new NVIDIA CUDA versions of BLAS and LAPACK routines to accelerate operations on batches of small, non-uniformly sized matrices, hybrid computing enhancements, support for multi-GPU acceleration, and further avoidance of PCIe communication through refinements to the sub-tree algorithm.  Back
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