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GTC ON-DEMAND

Deep Learning and AI
Presentation
Media
Abstract:
NVIDIA에서 전 세계에 있는 솔루션 아키텍처와 엔지니어링팀을 이끌고 있는 Marc Hamilton은 글로벌 고객과 파트너에게 인공지능, 딥 러닝, 프로페셔널 비주얼라이제이션, 그리고 고성능 컴퓨팅을 위한 세계 최고의 엔드 투 앤드(End-to-end) 솔루션을 제공하고 있습니다. 이번 키노트에서 가장 최신의 AI 기술 트렌드와 현재 NVIDIA가 어떻게 다양한 산업군에 걸쳐 AI 혁신을 가져오고 있는지 소개합니다 ...Read More
Abstract:
NVIDIA에서 전 세계에 있는 솔루션 아키텍처와 엔지니어링팀을 이끌고 있는 Marc Hamilton은 글로벌 고객과 파트너에게 인공지능, 딥 러닝, 프로페셔널 비주얼라이제이션, 그리고 고성능 컴퓨팅을 위한 세계 최고의 엔드 투 앤드(End-to-end) 솔루션을 제공하고 있습니다. 이번 키노트에서 가장 최신의 AI 기술 트렌드와 현재 NVIDIA가 어떻게 다양한 산업군에 걸쳐 AI 혁신을 가져오고 있는지 소개합니다  Back
 
Topics:
Deep Learning and AI
Type:
Keynote
Event:
AI Conference Korea
Year:
2019
Session ID:
SKR9101
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Deep Learning and AI Frameworks
Presentation
Media
Opening Keynote (Keynote Talk)
Abstract:
Don't miss this keynote from NVIDIA Founder & CEO, Jensen Huang, as he speaks on the future of computing.
 
Topics:
Deep Learning and AI Frameworks, Robotics, Intelligent Machines and IoT, Autonomous Vehicles, Data Center and Cloud Computing
Type:
Keynote
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9688
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AEC & Manufacturing
Presentation
Media
Abstract:
PEGATRON 的智慧家用機器人主要功能是可以利用深度學習發展的各種 AI 感知技術,例如人臉辨識、姿態偵測、火焰辨識等功能,來提供專屬家人的體驗及安全防護。其中還要介紹人臉辨識的發展過程,經過四年的發展,在 2017 我們達到 LFW 人臉辨識評估上 99.58% 的準確率。這些都是使用 NVIDIA GPU 進行訓練及部署。最後還會介紹 PEGATRON 在深度學習上發展的其他技術。 ...Read More
Abstract:
PEGATRON 的智慧家用機器人主要功能是可以利用深度學習發展的各種 AI 感知技術,例如人臉辨識、姿態偵測、火焰辨識等功能,來提供專屬家人的體驗及安全防護。其中還要介紹人臉辨識的發展過程,經過四年的發展,在 2017 我們達到 LFW 人臉辨識評估上 99.58% 的準確率。這些都是使用 NVIDIA GPU 進行訓練及部署。最後還會介紹 PEGATRON 在深度學習上發展的其他技術。  Back
 
Topics:
AEC & Manufacturing, Deep Learning and AI, Embedded
Type:
Talk
Event:
GTC Taiwan
Year:
2018
Session ID:
STW8017
Streaming:
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Abstract:
晶圓級晶粒尺寸封裝是錫凸塊將訊號直接由晶粒傳至電路板。在產品生命週期中這些錫接點會反覆受到高低溫變化造成的機械應力。錫接點的疲勞破裂便是封裝常見的失效模式。本研究使用 ANSYS Mechanical 模擬每個錫接點在五個溫度循環中的累積應變能增量,累積應變能增量較小的設計可預期會有較長壽命。此數值模型中同時有細微的晶粒特徵與相對大尺寸的電路板因此元素與節點會較一般封裝體更多,同時非線性的彈塑計算也使求解時間更為漫長。使用多核心及開啟 GPU 加速運算的效能差異將會一併比較。 ...Read More
Abstract:
晶圓級晶粒尺寸封裝是錫凸塊將訊號直接由晶粒傳至電路板。在產品生命週期中這些錫接點會反覆受到高低溫變化造成的機械應力。錫接點的疲勞破裂便是封裝常見的失效模式。本研究使用 ANSYS Mechanical 模擬每個錫接點在五個溫度循環中的累積應變能增量,累積應變能增量較小的設計可預期會有較長壽命。此數值模型中同時有細微的晶粒特徵與相對大尺寸的電路板因此元素與節點會較一般封裝體更多,同時非線性的彈塑計算也使求解時間更為漫長。使用多核心及開啟 GPU 加速運算的效能差異將會一併比較。  Back
 
Topics:
AEC & Manufacturing, Deep Learning and AI
Type:
Talk
Event:
GTC Taiwan
Year:
2018
Session ID:
STW8018
Streaming:
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Abstract:
英業達和子公司英研智能移動採用最新的深度學習物件偵測技術,包含 Faster R-CNN (FRCN) 和 Single Shot Multibox Detector (SSD) ,並針對 NVIDIA Jetson TX2 平台進行速度最佳化,故可在邊緣裝置上做到近 30 fps 即時且準確的物件偵測。我們把這個物件偵測技術運用在智慧路燈攝像頭上,不需將案場影像透過網路傳到遠端,而可在邊緣裝置上直接分析影像並自動偵測違規停車、計算人流、車流以及船隻等。 在這個演講中我們將分享在 NVIDIA Jetson TX2 平台上開發上述物件偵測技術的經驗及其在智慧路燈攝像頭上的應用。 ...Read More
Abstract:
英業達和子公司英研智能移動採用最新的深度學習物件偵測技術,包含 Faster R-CNN (FRCN) 和 Single Shot Multibox Detector (SSD) ,並針對 NVIDIA Jetson TX2 平台進行速度最佳化,故可在邊緣裝置上做到近 30 fps 即時且準確的物件偵測。我們把這個物件偵測技術運用在智慧路燈攝像頭上,不需將案場影像透過網路傳到遠端,而可在邊緣裝置上直接分析影像並自動偵測違規停車、計算人流、車流以及船隻等。 在這個演講中我們將分享在 NVIDIA Jetson TX2 平台上開發上述物件偵測技術的經驗及其在智慧路燈攝像頭上的應用。  Back
 
Topics:
AEC & Manufacturing, Embedded, Deep Learning and AI
Type:
Tutorial
Event:
GTC Taiwan
Year:
2018
Session ID:
STW8010
Streaming:
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Abstract:
精確的地圖資訊與可預測的交通路況資料對於智慧交通系統 (ITS)
的成敗至關重要。傳統製圖產業倚重大量的人力與時間,因此大部分的導航系統中的地圖通常會有過時的問題。另一方面,當前大部分的交通流量預測方法以統計方法建立路況預測模型,因此無法滿足許多現實世界的應用情境。這讓我們不得不重新思考如何透過深度學習的架構,搭配我們大量的地圖和歷史交通數據去加速製圖流程以及進行交通路況的預測工作。勤崴已經在 NVIDIA 的軟、硬體平台上進行開發研究,將深度學習技術運用在智慧化製圖與 AI 智慧導航上,逐步建構出未來智慧城市下的 ITS 應用情境。 ...Read More
Abstract:
精確的地圖資訊與可預測的交通路況資料對於智慧交通系統 (ITS)
的成敗至關重要。傳統製圖產業倚重大量的人力與時間,因此大部分的導航系統中的地圖通常會有過時的問題。另一方面,當前大部分的交通流量預測方法以統計方法建立路況預測模型,因此無法滿足許多現實世界的應用情境。這讓我們不得不重新思考如何透過深度學習的架構,搭配我們大量的地圖和歷史交通數據去加速製圖流程以及進行交通路況的預測工作。勤崴已經在 NVIDIA 的軟、硬體平台上進行開發研究,將深度學習技術運用在智慧化製圖與 AI 智慧導航上,逐步建構出未來智慧城市下的 ITS 應用情境。  Back
 
Topics:
AEC & Manufacturing, Deep Learning and AI
Type:
Talk
Event:
GTC Taiwan
Year:
2018
Session ID:
STW8024
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Abstract:
我們提出一個泛用且基於深度學習的 360 度視覺感知系統,用來達到顯著偵測、自我定位、以及深度預測。這些功能的完成,是基於一個新開發的通用 Padding 技術 (Cube-Padding)。此技術可以泛用在任何卷積類神經網路上,達到避免 360 度影像原始的扭曲以及圖像邊緣等問題。360 度的視覺感知系統是任何自主系統的核心技術,例如自動駕駛車、四軸飛行器、或是家用機器人對 360 度環境的感知。 ...Read More
Abstract:
我們提出一個泛用且基於深度學習的 360 度視覺感知系統,用來達到顯著偵測、自我定位、以及深度預測。這些功能的完成,是基於一個新開發的通用 Padding 技術 (Cube-Padding)。此技術可以泛用在任何卷積類神經網路上,達到避免 360 度影像原始的扭曲以及圖像邊緣等問題。360 度的視覺感知系統是任何自主系統的核心技術,例如自動駕駛車、四軸飛行器、或是家用機器人對 360 度環境的感知。  Back
 
Topics:
AEC & Manufacturing, Deep Learning and AI, Embedded
Type:
Talk
Event:
GTC Taiwan
Year:
2018
Session ID:
STW8025
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Abstract:
隨著 AI 技術的演進,眾人均期待以機器視覺輔佐智慧製造,達到高效能、精準、快速反應的智慧製造需求。 機器視覺的應用範疇將不再僅受限於量測、定位、導引、辨識等傳統應用,如何一方面加入 AI 的頭腦,即時收集、分析與處理大量資料運算,另一方面並能與其他設備協同工作,與時俱進支援邊緣運算,以及建構智慧工廠所必需的實時決策系統,乃成為新興話題與挑戰。凌華科技將與您共同探討此一方向。 ...Read More
Abstract:
隨著 AI 技術的演進,眾人均期待以機器視覺輔佐智慧製造,達到高效能、精準、快速反應的智慧製造需求。 機器視覺的應用範疇將不再僅受限於量測、定位、導引、辨識等傳統應用,如何一方面加入 AI 的頭腦,即時收集、分析與處理大量資料運算,另一方面並能與其他設備協同工作,與時俱進支援邊緣運算,以及建構智慧工廠所必需的實時決策系統,乃成為新興話題與挑戰。凌華科技將與您共同探討此一方向。  Back
 
Topics:
AEC & Manufacturing, Deep Learning and AI, Embedded
Type:
Talk
Event:
GTC Taiwan
Year:
2018
Session ID:
STW8026
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Abstract:
Learn about the unique challenges being solved using deep learning on GPUs in a large-scale mass customization of medical devices. Deep neural networks have been successfully applied to some of the most difficult problems in computer vision, nat ...Read More
Abstract:

Learn about the unique challenges being solved using deep learning on GPUs in a large-scale mass customization of medical devices. Deep neural networks have been successfully applied to some of the most difficult problems in computer vision, natural language processing, and robotics. But we still haven't seen the full potential of this technology used in manufacturing. Glidewell Labs daily produces thousands of patient specific items, such as dental restorations, implants, and appliances. Our goal is to make high-quality restorative dentistry affordable to more patients. This goal can only be achieved with flexible, highly autonomous CAD/CAM systems, which rely on AI for real-time decision making.

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Topics:
AEC & Manufacturing, Deep Learning and AI, Healthcare and Life Sciences
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7114
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Abstract:
Learn about a new solid modeling approach created to provide support for customer- and patient-specific design and additive manufacturing (3D printing) with graded materials and properties. The new modeling approach involves a hybrid of function-base ...Read More
Abstract:
Learn about a new solid modeling approach created to provide support for customer- and patient-specific design and additive manufacturing (3D printing) with graded materials and properties. The new modeling approach involves a hybrid of function-based (implicit) modeling and voxel modeling; models consist of function values on a regular grid (along with a simple interpolant), so meshing/triangulation of objects' surfaces and/or volumes is avoided. Learn the basic ideas behind the modeling approach and see demonstrations of: (1) CUDA-accelerated, real-time interactions between digital models imported from CAD systems and digitized/scanned models, (2) design and fabrication of objects with graded materials/properties, and (3) initial results of CUDA-accelerated methods for mesh-free property evaluation and analysis.  Back
 
Topics:
AEC & Manufacturing, Healthcare and Life Sciences
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7131
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Abstract:
Introducing the transition from traditional workstation to immersive experience workspace, hear about novel NVIDIA and ESI technologies to combine streaming and virtualization for GPUs to provide scalable immersive virtual and augmented reality. We' ...Read More
Abstract:
Introducing the transition from traditional workstation to immersive experience workspace, hear about novel NVIDIA and ESI technologies to combine streaming and virtualization for GPUs to provide scalable immersive virtual and augmented reality. We'll discuss the challenges in advancing to the immersive workspace for mobile, desk-side, or team-size immersive experiences through on-premise and cloud-based virtual engineering applications.  Back
 
Topics:
AEC & Manufacturing, GPU Virtualization, Virtual Reality and Augmented Reality
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7203
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Abstract:
Honda's evolutionary new project?internally called the "Next-gen Engineering Workstation (EWS) Project"?is designed to optimize usage of our CAD-VDI environment for R&D offices and factories. The project's challenges are to ...Read More
Abstract:

Honda's evolutionary new project?internally called the "Next-gen Engineering Workstation (EWS) Project"?is designed to optimize usage of our CAD-VDI environment for R&D offices and factories. The project's challenges are to move from the existing physical EWS and pass-through VDI environments to an NVIDIA GRID vGPU environment. All while improving user density (CCU/server), usage monitoring, resource optimization for designers, and flexible resource reallocation. Honda successfully deployed more than 4,000 concurrent CAD-VDI users in its initial phase, with aggressive plans to further increase utilization. This session will review the project's challenges and Honda's future vision.

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Topics:
AEC & Manufacturing, GPU Virtualization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7390
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Abstract:
Improvements in 3D printing allow for unique processes, finer details, better quality control, and a wider range of materials as printing hardware improves. With these improvements comes the need for greater computational power and control over 3D-pr ...Read More
Abstract:
Improvements in 3D printing allow for unique processes, finer details, better quality control, and a wider range of materials as printing hardware improves. With these improvements comes the need for greater computational power and control over 3D-printed objects. We introduce NVIDIA GVDB Voxels as an open source SDK for voxel-based 3D printing workflows. Traditional workflows are based on processing polygonal models and STL files for 3D printing. However, such models don't allow for continuous interior changes in color or density, for descriptions of heterogeneous materials, or for user-specified support lattices. Using the new NVIDIA GVDB Voxels SDK, we demonstrate practical examples of design workflows for complex 3D printed parts with high-quality ray-traced visualizations, direct data manipulation, and 3D printed output.  Back
 
Topics:
AEC & Manufacturing, AEC Industries
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7425
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Abstract:
We'll present how deep learning is applied in a manufacturer's production line. Fujikura and OPTOENERGY are introducing a visual inspection system incorporating deep learning in the production process of semiconductor lasers. The same in ...Read More
Abstract:

We'll present how deep learning is applied in a manufacturer's production line. Fujikura and OPTOENERGY are introducing a visual inspection system incorporating deep learning in the production process of semiconductor lasers. The same inspection accuracy as skilled workers was achieved by optimizing the image size and the hyper parameters of a CNN model. The optimized image size is less than one quarter of the image size required for the visual inspection by skilled workers, which leads to large cost reduction of the production line. It was also confirmed that the highlighted region in the heatmaps of NG images didn't meet the criteria of the visual inspection. The visual inspection incorporating deep learning is being applied to other products such as optical fibers and electrical cables.

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Topics:
AEC & Manufacturing, Deep Learning and AI, Intelligent Machines and IoT
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7623
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AEC Industries
Presentation
Media
Abstract:
大型 AEC 项目涉及复杂结构的设计与验证工作,但也需通过 VR 来让用户身临其境地感受和理解建筑空间的实际大小。SHoP Architects 是 Dassault Systèmes 3DEXPERIENCE 的早期采用者之一,该平台无需使用其他外部工具便可将 CATIA CAD 领域 20 多年积累的优秀成果与先进的渲染材质(包括 Substance 支持)结合在一起,并可提供原生 VR 沉浸式体验。本次报告将展示 SHoP 主要借助 GPU 实现的最新成果,包括如何使用上述平台模拟建筑施工、通过 VR SLI 以 90 FPS 帧速率渲染大型网格以显著提高性能,从而实现多用户多地点 VR 评审,以及通过集成 Substance 来开展逼真的 AEC 设计评审 ...Read More
Abstract:
大型 AEC 项目涉及复杂结构的设计与验证工作,但也需通过 VR 来让用户身临其境地感受和理解建筑空间的实际大小。SHoP Architects 是 Dassault Systèmes 3DEXPERIENCE 的早期采用者之一,该平台无需使用其他外部工具便可将 CATIA CAD 领域 20 多年积累的优秀成果与先进的渲染材质(包括 Substance 支持)结合在一起,并可提供原生 VR 沉浸式体验。本次报告将展示 SHoP 主要借助 GPU 实现的最新成果,包括如何使用上述平台模拟建筑施工、通过 VR SLI 以 90 FPS 帧速率渲染大型网格以显著提高性能,从而实现多用户多地点 VR 评审,以及通过集成 Substance 来开展逼真的 AEC 设计评审。  Back
 
Topics:
AEC Industries, VR and Simulation
Type:
Talk
Event:
GTC China
Year:
2018
Session ID:
CH81001
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Abstract:
新型视觉计算技术正在改变建筑和城市的设计方式。无论是在设计评审和客户展示阶段,还是在设计流程的早期阶段,设计公司都在更广泛地应用照片级逼真度,以帮助改进设计决策。虚拟现实有助我们更加清晰地了解设计评审,而机器学习和深度学习让图像分析、预测和自然语言处理在工程应用中得以实现。本次讲座将回顾这些突破性技术如何促进设计团队实现创新,并将涵盖在工程领域部署和推广机器学习的趋势与挑战 ...Read More
Abstract:
新型视觉计算技术正在改变建筑和城市的设计方式。无论是在设计评审和客户展示阶段,还是在设计流程的早期阶段,设计公司都在更广泛地应用照片级逼真度,以帮助改进设计决策。虚拟现实有助我们更加清晰地了解设计评审,而机器学习和深度学习让图像分析、预测和自然语言处理在工程应用中得以实现。本次讲座将回顾这些突破性技术如何促进设计团队实现创新,并将涵盖在工程领域部署和推广机器学习的趋势与挑战。  Back
 
Topics:
AEC Industries
Type:
Talk
Event:
GTC China
Year:
2018
Session ID:
CH81002
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Abstract:
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
Abstract:
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
 
Topics:
AEC Industries, GPU Virtualization, Data Center and Cloud Infrastructure
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7174
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Abstract:
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
Abstract:
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
 
Topics:
AEC Industries, AEC & Manufacturing, Rendering and Ray Tracing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7474
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Abstract:
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
Abstract:
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
 
Topics:
AEC Industries, Deep Learning and AI, AI Startup
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7575
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Abstract:
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
Abstract:

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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Topics:
AEC Industries, Virtual Reality and Augmented Reality
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7614
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AI Application Deployment and Inference
Presentation
Media
Abstract:
This talk addresses how enterprises can reduce risk by taking a practical approach with their AI initiatives from conceptualization to deployment by evaluating business value, managing open source effectively, creating development environments that e ...Read More
Abstract:
This talk addresses how enterprises can reduce risk by taking a practical approach with their AI initiatives from conceptualization to deployment by evaluating business value, managing open source effectively, creating development environments that economically scale over time as the needs grow, and creating effective workflows based on data movement. By taking this approach to AI, enterprises customers can make incremental investments for success while overcoming the complexity of this new technology  Back
 
Topics:
AI Application Deployment and Inference, AI and DL Business Track (high level)
Type:
Sponsored Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S91000
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Abstract:
Advancements in the field of artificial intelligence, particularly in the area of deep learning, have facilitated training and inferencing to bring new applications to every aspect of our lives. This session is designed to bridge the gap by identifyi ...Read More
Abstract:
Advancements in the field of artificial intelligence, particularly in the area of deep learning, have facilitated training and inferencing to bring new applications to every aspect of our lives. This session is designed to bridge the gap by identifying what is required to apply AI to a range of new types of workloads.  Back
 
Topics:
AI Application Deployment and Inference
Type:
Sponsored Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S91007
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Abstract:
Modern AI methods represent a great opportunity to rethink every aspect of our companies, from engineering products, to pricing them, to production, and even to basic tasks like human resources and finance. In this talk, we will talk about some use c ...Read More
Abstract:
Modern AI methods represent a great opportunity to rethink every aspect of our companies, from engineering products, to pricing them, to production, and even to basic tasks like human resources and finance. In this talk, we will talk about some use cases in the enterprise, and then the software tools and hardware infrastructure required to build successful AI-based applications. We will talk about some of ways IBM is enhancing open-source software like Tensorflow and pyTorch and also how automatic AI (AutoAI) will enable faster creation and deployment of AI models.  Back
 
Topics:
AI Application Deployment and Inference, AI and DL Business Track (high level)
Type:
Sponsored Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S91033
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Abstract:
Exploring the Best Server for AI Speaker: Samuel D. Matzek, Sr. Software Engineer Speaker: Maria Ward, IBM Accelerated Server Offering Manager Explore the server at the heart of the Summit and Sierra supercomputers, and the best server for ...Read More
Abstract:

Exploring the Best Server for AI Speaker: Samuel D. Matzek, Sr. Software Engineer Speaker: Maria Ward, IBM Accelerated Server Offering Manager Explore the server at the heart of the Summit and Sierra supercomputers, and the best server for AI. We will discuss the technical details that set this server apart and why it matters for your machine learning and deep learning workloads. IBM Cloud for AI at Scale Speaker: Alex Hudak, IBM Cloud Offering Manager AI is fast changing the modern enterprise with new applications that are resource demanding, but provide new capabilities to drive insight from customer data. IBM Cloud is partnering with NVIDIA to provide a world class and customized cloud environment to meet the needs of these new applications. Learn about the wide range of NVIDIA GPU solutions inside the IBM Cloud virtual and bare metal server portfolio, and how customers are using them across Deep Learning, Analytics, HPC workloads, and more. IBM Spectrum LSF Family Overview & GPU Support Speaker: Larry Adams, Global Architect - Cross Sector, Developer, Consultant, IBM Systems How to Fuel the Data Pipeline Speaker: Kent Koeninger, IBM IBM Storage Reference Architecture for AI with Autonomous Driving Speaker: Kent Koeninger, IBM  

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Topics:
AI Application Deployment and Inference
Type:
Sponsored Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S91053
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Abstract:
Learn how recent achievements in machine learning, sensor fusion, and GPU computing make it possible to create a next-generation advanced driver-assistance systems experience. We'll showcase a software solution that creates real-time augmented reali ...Read More
Abstract:
Learn how recent achievements in machine learning, sensor fusion, and GPU computing make it possible to create a next-generation advanced driver-assistance systems experience. We'll showcase a software solution that creates real-time augmented reality for drivers while using vehicle sensors, map data, telematics, and navigation guidance with a set of advanced algorithms. Our approach augments drivers' visual reality with supplementary objects in real time, and works with output devices such as head unit displays, digital clusters, and head-up displays. We'll also examine the challenges of running advanced neural network models in real time on embedded hardware and explain solutions to overcome them.  Back
 
Topics:
AI Application Deployment and Inference, Virtual Reality and Augmented Reality, Autonomous Vehicles
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9169
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Abstract:
A common deep learning workload is batch processing of videos to identify objects an image. We'll show examples of how to deploy a style-transfer and object-detection model on a cluster of V100 GPUs using Dask. Dask allows us develop the logic of ou ...Read More
Abstract:
A common deep learning workload is batch processing of videos to identify objects an image. We'll show examples of how to deploy a style-transfer and object-detection model on a cluster of V100 GPUs using Dask. Dask allows us develop the logic of our processing pipeline locally and deploy it on a cluster without having to rewrite anything. We'll discuss how we integrate it into Azure ML pipelines, as well as how to deploy it on a Kubernetes cluster for a scalable solution.  Back
 
Topics:
AI Application Deployment and Inference, Deep Learning and AI Frameworks
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9198
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Abstract:
We'll present a fast, highly accurate, and customizable object-detection network optimized for training and inference on GPUs. After describing the network architecture, we'll dive into how different stages of training workflow are accelerated. Our ...Read More
Abstract:
We'll present a fast, highly accurate, and customizable object-detection network optimized for training and inference on GPUs. After describing the network architecture, we'll dive into how different stages of training workflow are accelerated. Our techniques include data ingestion and augmentation, mixed precision, and multi-GPU training. We'll demonstrate how we optimized our network for deployment without loss of accuracy using ONNX and NVIDIA TensorRT. We'll also show how to create TensorRT plugins for post-processing to perform inference entirely on the GPU. This session will be a combination of lecture and demos.  Back
 
Topics:
AI Application Deployment and Inference, Deep Learning and AI Frameworks, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9243
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Abstract:
We'll discuss our work at Esri to reconstruct 3D building models from aerial LiDAR data with the help of deep neural networks. The value of accurate 3D building models for cities is hard to overestimate, but collecting and maintaining this data is l ...Read More
Abstract:
We'll discuss our work at Esri to reconstruct 3D building models from aerial LiDAR data with the help of deep neural networks. The value of accurate 3D building models for cities is hard to overestimate, but collecting and maintaining this data is labor-intensive, error-prone, and expensive. We teamed up with Miami-Dade County and NVIDIA to see if we could streamline this data-acquisition workflow or at least, make it more cost-effective. We used a Mask R-CNN model trained to detect and report instances of roof segments of various types. Our talk will cover data preparation and Mask R-CNN training and achieved precision. We'll also outline the inference architecture, the integration of TensorFlow and ArcGIS Pro 2.3, and the steps we used to reconstruct 3D building models from the predictions.  Back
 
Topics:
AI Application Deployment and Inference, Seismic and Geosciences, Product & Building Design
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9255
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Abstract:
Take a journey through the TensorFlow container provided by the NVIDIA GPU Cloud. We'll start with how to launch and navigate inside the container, and stop along the way to explore the included demo scripts, extend the container with extra ...Read More
Abstract:

Take a journey through the TensorFlow container provided by the NVIDIA GPU Cloud. We'll start with how to launch and navigate inside the container, and stop along the way to explore the included demo scripts, extend the container with extra software, and examine best practices for how to take advantage of all the benefits bundled inside the NGC TensorFlow container. This session will help NGC beginners get the most out of the TensorFlow container and become productive as quickly as possible.

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Topics:
AI Application Deployment and Inference, Deep Learning and AI Frameworks
Type:
Tutorial
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9256
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Abstract:
Learn how GPU Coder produces high-performance CUDA code automatically from a high-level algorithm description in MATLAB. Write your deep learning application with the expressive power of MATLAB, which allows you to describe not just the use of your t ...Read More
Abstract:
Learn how GPU Coder produces high-performance CUDA code automatically from a high-level algorithm description in MATLAB. Write your deep learning application with the expressive power of MATLAB, which allows you to describe not just the use of your trained deep learning model in inference mode, but also perform data-augmentation and post-processing of the results to create a complete deployment-ready application. With MATLAB running on your host machine, communicate and control peripheral devices on your Jetson Xavier and DRIVE Xavier platforms to bring in live data from sensors for visualization and analysis. GPU Coder can then generate optimized inference code for the whole application. The deep learning inference model is compiled down to TensorRT's inference engine, while the rest of the application logic is parallelized through creation of CUDA kernels and integrated with other CUDA optimized libraries like cuBLAS, cuFFT, etc. GPU Coder provides a clean, elegant solution to go from algorithm to application deployment, unleashing the performance of CUDA, TensorRT, and the Xavier device architecture.  Back
 
Topics:
AI Application Deployment and Inference, Deep Learning and AI Frameworks, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9281
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Abstract:
Learn how to develop faster, scalable, and better GPU-Accelerated distributed inference on multi-node and multi-GPU cluster environments. In most of the benchmark cases, linear scalability for throughput performance is not guaranteed with increasing ...Read More
Abstract:
Learn how to develop faster, scalable, and better GPU-Accelerated distributed inference on multi-node and multi-GPU cluster environments. In most of the benchmark cases, linear scalability for throughput performance is not guaranteed with increasing the number of GPUs and servers. We'll discuss present an efficient scale-out method for deploying deep learning-based object detection models on multi-node and multi-GPU clusters using Apache Hadoop and Spark. We'll explain how to deploy 120 deep learning models (YOLOv2) on our own video datasets with NVIDIA Tesla M60 GPU (30EA) Hadoop cluster. Our approach achieved about 20 percent faster inference throughput with super-linear scalability from one GPU server to 30 GPU cluster. This session will be a combination of lecture and videos about our GPU-Accelerated distributed inference platform for large-scale streaming data analytics.  Back
 
Topics:
AI Application Deployment and Inference, Intelligent Video Analytics, Video and Image Processing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9343
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Abstract:
We'll talk about how we built a GPU-Accelerated system for real-time information retrieval from large datasets in life sciences. Unstructured textual data is full of phrases and words that have multiple meanings, making it difficult for current info ...Read More
Abstract:
We'll talk about how we built a GPU-Accelerated system for real-time information retrieval from large datasets in life sciences. Unstructured textual data is full of phrases and words that have multiple meanings, making it difficult for current information-retrieval algorithms to find relevant documents. We'll describe our knowledge graph-based filtering mechanism for more precise real-time information retrieval. We outline how we accelerated the embedding generation process, treating it as an optimization problem and running it on NVIDIA Tesla V100 GPU cores. We'll also cover how we reduced the latency in distance computation using TensorRT.  Back
 
Topics:
AI Application Deployment and Inference, Speech and Language Processing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9359
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Abstract:
Learn how recent advances in AI can be used to map informal settlements, or slums, in developing countries. We'll show how slums can be mapped using machine learning with noisy annotations and multi-resolution, multi-spectral data. We'll discuss an ...Read More
Abstract:
Learn how recent advances in AI can be used to map informal settlements, or slums, in developing countries. We'll show how slums can be mapped using machine learning with noisy annotations and multi-resolution, multi-spectral data. We'll discuss an effective end-to-end framework that detects and maps the locations of informal settlements using low-resolution, freely available Sentinel-2 satellite imagery. Our talk will examine different approaches based on multi-spectral information to identify roofing material types and show how our work can be used for slums all over the world. We'll also describe how multi-spectral, multi-resolution and multi-temporal satellite imagery can be used during natural disasters to quantify the impact on urban infrastructure. This session presents research undertaken in the NASA and ESA Frontier Development Lab.  Back
 
Topics:
AI Application Deployment and Inference, AI and DL Research, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9362
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Abstract:
We'll describe our work at Intelligent Voice on explainable AI. We are working to separate AI technology into smaller components so it can be more easily explained, build explainability into AI architecture design, and make it possible for A ...Read More
Abstract:

We'll describe our work at Intelligent Voice on explainable AI. We are working to separate AI technology into smaller components so it can be more easily explained, build explainability into AI architecture design, and make it possible for AI to progress within confines of current regulation. New GDPR regulations in Europe, which affect any company with European consumers, give people a right to challenge computer-aided decisions and to have these decisions explained. We'll discuss how existing technology can make it difficult to provide an explanation and how that inhibits AI adoption in customer-facing fields such as insurance, health, and financial services.

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Topics:
AI Application Deployment and Inference, Advanced AI Learning Techniques
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9392
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Abstract:
We will describe a new API that more effectively utilizes the GPU hardware for multiple single inference instances of the same RNN model. Many NLP applications have real-time run time requirements for multiple independent inference instances. Our pro ...Read More
Abstract:
We will describe a new API that more effectively utilizes the GPU hardware for multiple single inference instances of the same RNN model. Many NLP applications have real-time run time requirements for multiple independent inference instances. Our proposed API accepts independent inference requests from an application and seamlessly combines them to a large batch execution. Time steps from independent inference tasks are combined together so that we achieve high performance while staying within the latency budgets of an application for a time step. We also discuss functionality that allows the user to wait on completion of a certain time step, a task that's possible because our implementation is mainly composed of non-blocking function calls. Finally, we'll present performance data from the Turing architecture for an example RNN model with LSTM cells and projections.  Back
 
Topics:
AI Application Deployment and Inference
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9422
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Abstract:
We'll explain how to use TensorRT via TensorFlow and/or TensorFlow serving. TensorFlow is a flexible, high-performance software library for numerical computation using data flow graphs and NVIDIA TensorRT is a platform for high-performance deep lear ...Read More
Abstract:
We'll explain how to use TensorRT via TensorFlow and/or TensorFlow serving. TensorFlow is a flexible, high-performance software library for numerical computation using data flow graphs and NVIDIA TensorRT is a platform for high-performance deep learning inference. We'll describe how TensorRT is integrated with TensorFlow and show how combining the two improves efficiency of machine learning models. We'll also use examples to show how to use the integration.  Back
 
Topics:
AI Application Deployment and Inference
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9431
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Abstract:
As the use of AI has increased, so has the need for a production-quality AI inference solution. We'll discuss the latest additions to NVIDIA's TensorRT Inference Server and describe deployment examples to help plan your data center production infer ...Read More
Abstract:
As the use of AI has increased, so has the need for a production-quality AI inference solution. We'll discuss the latest additions to NVIDIA's TensorRT Inference Server and describe deployment examples to help plan your data center production inference architecture. NVIDIA TensorRT Inference Server makes it possible to efficiently leverage inference in applications and to do so without reinventing the wheel. We'll talk about how TensorRT supports the top AI frameworks and custom backends, and maximizes utilization by hosting multiple models per GPU and across GPUs with dynamic request batching. Our talk will also cover how the inference server seamlessly supports Kubernetes with health and latency metrics and integrates with Kubeflow for simplified deployment.   Back
 
Topics:
AI Application Deployment and Inference, Data Center and Cloud Infrastructure
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9438
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Abstract:
Building machine learning pipelines is challenging. Doing that in a portable way that supports multi-cloud deployments is even harder. We'll discuss the open source project, Kubeflow, which is designed to allow data scientists and machine learning e ...Read More
Abstract:
Building machine learning pipelines is challenging. Doing that in a portable way that supports multi-cloud deployments is even harder. We'll discuss the open source project, Kubeflow, which is designed to allow data scientists and machine learning engineers to focus on building great ML solutions instead of setting up and managing the infrastructure. We'll detail the latest version of Kubeflow and its integration with TensorRT, the inference server from NVIDIA.  Back
 
Topics:
AI Application Deployment and Inference, Data Center and Cloud Infrastructure, Tools and Libraries
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9456
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Abstract:
We'll explore the practical challenges involved in developing a deep learning and artificial intelligence platform for satellite imagery analysis. Our talk will cover technical challenges we faced, platform design considerations, and best practices ...Read More
Abstract:
We'll explore the practical challenges involved in developing a deep learning and artificial intelligence platform for satellite imagery analysis. Our talk will cover technical challenges we faced, platform design considerations, and best practices learned from implementing our analytics platform. Learn how to modify a deep learning model for better throughput by combining TensorFlow and TensorRT with the right hardware and software platform options. We'll explain how to design architecture to inference huge amounts of imagery using TensorRT and discuss business opportunities for the defense and financial industries that arise from watching whole world with a global dataset.  Back
 
Topics:
AI Application Deployment and Inference, Seismic and Geosciences
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9459
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Abstract:
Kubeflow has emerged as the de facto way to do ML on Kubernetes. Learn about the fastest way to go from GPU machines to an operational Kubeflow cluster on any public or private cloud, bare metal, or even your own laptop. We'll demonstrate the tools ...Read More
Abstract:
Kubeflow has emerged as the de facto way to do ML on Kubernetes. Learn about the fastest way to go from GPU machines to an operational Kubeflow cluster on any public or private cloud, bare metal, or even your own laptop. We'll demonstrate the tools and steps necessary to quickly deploy Kubeflow at any scale. We'll also show you a fast and exciting new way to deploy Kubeflow on a single laptop or desktop, which is perfect for local ML development. Don't lose time deploying and configuring Kubernetes and Kubeflow get to the ML as quickly as possible.  Back
 
Topics:
AI Application Deployment and Inference, Deep Learning and AI Frameworks
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9515
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Abstract:
Learn about the advantages and pitfalls of venturing away from off-the-shelf libraries to implement neural network inference algorithms from the ground up. We'll discuss the challenges of building large-vocabulary speech recognition engines able to ...Read More
Abstract:
Learn about the advantages and pitfalls of venturing away from off-the-shelf libraries to implement neural network inference algorithms from the ground up. We'll discuss the challenges of building large-vocabulary speech recognition engines able to support decoding more than 1,000 simultaneous conversations per NVIDIA V100 card, while still able to down-port onto low-memory embedded configurations such as the Tegra TK1. We'll cover what characteristics of the many types of popular neural networks used in speech recognition scale almost perfectly, as well as those that resist scaling and even scale negatively. Learn what profiling reveals about the silent, looming cost of kernel synchronization and what to do about it.  Back
 
Topics:
AI Application Deployment and Inference, Speech and Language Processing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9535
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Abstract:
We'll share our experience building an audio cognition platform that extracts non-verbal information such as speech, music, and environmental sounds. Cochlear.ai's Sense platform leverages acoustic event detection, scene classification, human gende ...Read More
Abstract:
We'll share our experience building an audio cognition platform that extracts non-verbal information such as speech, music, and environmental sounds. Cochlear.ai's Sense platform leverages acoustic event detection, scene classification, human gender/age estimation, music analysis, and more with near real-time analysis from audio data. Everything is optimized for audio processing, including the cloud backend, API design and management, and deep learning architecture. We'll detail our learnings and challenges in developing our audio cognition service.  Back
 
Topics:
AI Application Deployment and Inference, AI and DL Research, Deep Learning and AI Frameworks
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9625
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Abstract:
You may already use NVIDIA's cuDNN library to accelerate your deep neural network inference, but are you getting the most out of it to truly unleash the tremendous performance of NVIDIA's newest GPU architectures, Volta and Turing? We'll discuss h ...Read More
Abstract:
You may already use NVIDIA's cuDNN library to accelerate your deep neural network inference, but are you getting the most out of it to truly unleash the tremendous performance of NVIDIA's newest GPU architectures, Volta and Turing? We'll discuss how to avoid the most common pitfalls in porting your CPU-based inference to the GPU and demonstrate best practices in a step-by-step optimization of an example network. Learn how to deploy your deep neural network inference in both the fastest and most memory-efficient way, using cuDNN and Tensor Cores, NVIDIA's revolutionary technology that delivers groundbreaking performance in FP16, INT8 and INT4 inference on Volta and Turing.  Back
 
Topics:
AI Application Deployment and Inference, Deep Learning and AI Frameworks
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9644
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Abstract:
Learn how to bring the power of convolutional neural networks and deep learning to memory- and power-constrained devices like smartphones, wearable devices, and drones. We'll show these techniques at work on real-world project and discuss tips and t ...Read More
Abstract:
Learn how to bring the power of convolutional neural networks and deep learning to memory- and power-constrained devices like smartphones, wearable devices, and drones. We'll show these techniques at work on real-world project and discuss tips and tricks, speed and accuracy trade-offs, and benchmarks on different hardware. We will then demonstrate how to get started developing your own deep learning application for storage- and power-constrained mobile devices. We'll also discuss how to apply similar techniques to increase deep neural net efficiency when deploying in a regular cloud-based production environment. This approach reduces the number of GPUs required and lowers cost.  Back
 
Topics:
AI Application Deployment and Inference, AI and DL Research
Type:
Tutorial
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9648
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Abstract:
We'll discuss Project MagLev, NVIDIA's internal end-to-end AI platform for developing its self-driving car software, DRIVE. We'll explore the platform that supports continuous data ingest from multiple cars producing TB of data per h ...Read More
Abstract:

We'll discuss Project MagLev, NVIDIA's internal end-to-end AI platform for developing its self-driving car software, DRIVE. We'll explore the platform that supports continuous data ingest from multiple cars producing TB of data per hour. We'll also cover how the platform enables autonomous AI designers to iterate training of new neural network designs across thousands of GPU systems and validate the behavior of these designs over multi PB-scale data sets. We will talk about our overall architecture for everything from data center deployment to AI pipeline automation, as well as large-scale AI dataset management, AI training, and testing.

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Topics:
AI Application Deployment and Inference, Autonomous Vehicles, Data Center and Cloud Infrastructure
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9649
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Speakers:
Abstract:
Although neural network training is typically done in either 32- or 16-bit floating point formats, inference can be run at even lower precisions that reduce memory footprint and elapsed time. We'll describe quantizing neural networks models for vari ...Read More
Abstract:
Although neural network training is typically done in either 32- or 16-bit floating point formats, inference can be run at even lower precisions that reduce memory footprint and elapsed time. We'll describe quantizing neural networks models for various image (classification, detection, segmentation) and natural language processing tasks. In addition to convolutional feed forward networks, we will cover quantization of recurrent models. The discussion will examine both floating point and integer quantizations, targeting features in Volta and Turing GPUs.  Back
 
Topics:
AI Application Deployment and Inference, AI and DL Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9659
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Abstract:
There are numerous problems which have been exposed by the creation of AI models due to the total capability of the current generation of GPUs to create and run a large volume of models, and we are going to show people how to fix them. The exponentia ...Read More
Abstract:
There are numerous problems which have been exposed by the creation of AI models due to the total capability of the current generation of GPUs to create and run a large volume of models, and we are going to show people how to fix them. The exponential compute growth which has occurred in this area has opened the doors to creating and testing hundreds or thousands more models than the, one-by-one approach which was performed in the past. These models use and generate data from both batch and real-time sources. As data becomes enriched, and parameters tuned and explored, there is a need for versioning everything, including the data. Issues found here are similar to other software engineering problems, but new approaches must be taken to create solutions given the complexity of the problems with the inclusion of vast amounts of data. We will discuss the very specific problems and approaches to fix them.  Back
 
Topics:
AI Application Deployment and Inference, Data Center and Cloud Infrastructure
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9683
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Abstract:
We'll discuss network quantization its background, methods, achievements, and the motivation behind it. Deep neural networks have achieved remarkable performance in a wide range of tasks. But DNNs are computationally intensive and resource-consuming ...Read More
Abstract:
We'll discuss network quantization its background, methods, achievements, and the motivation behind it. Deep neural networks have achieved remarkable performance in a wide range of tasks. But DNNs are computationally intensive and resource-consuming, which hinders their use in embedded systems. We'll explain how we're working to alleviate this problem with quantized neural networks and a lightweight framework for efficient inference of these networks.  Back
 
Topics:
AI Application Deployment and Inference
Type:
Tutorial
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9713
Streaming:
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Abstract:
Many computer vision applications powered by deep learning include multi-stage pre-processing data pipelines with compute-intensive processes like decoding, cropping, and format conversion that are carried out on CPUs. We'll discuss NVIDIA DALI, an ...Read More
Abstract:
Many computer vision applications powered by deep learning include multi-stage pre-processing data pipelines with compute-intensive processes like decoding, cropping, and format conversion that are carried out on CPUs. We'll discuss NVIDIA DALI, an open source, GPU-Accelerated data augmentation and image-loading library for optimizing data pipelines of deep learning frameworks. DALI provides a full pre- and post-processing data pipeline ready for training and inference. We'll demonstrate a TensorRT inference workflow within DALI-configurable graphs as well as custom operators.  Back
 
Topics:
AI Application Deployment and Inference, Deep Learning and AI Frameworks
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9818
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Abstract:
We'll demonstrate the use of Nsight Systems to quickly identify bottlenecks and achieve significant speedups in production workflows at Facebook. We'll also describe how we use the CUPTI API for on-demand, customized timeline analysis of workflows ...Read More
Abstract:
We'll demonstrate the use of Nsight Systems to quickly identify bottlenecks and achieve significant speedups in production workflows at Facebook. We'll also describe how we use the CUPTI API for on-demand, customized timeline analysis of workflows running in production and collect detailed performance metrics across our GPU fleet at very low overhead.  Back
 
Topics:
AI Application Deployment and Inference
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9866
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Abstract:
We'll discuss how human rights organizations can use AI to investigate human rights violations around the world. With a focus on deep learning and satellite imagery, we'll examine potential applications, including signature detection of arson, hous ...Read More
Abstract:
We'll discuss how human rights organizations can use AI to investigate human rights violations around the world. With a focus on deep learning and satellite imagery, we'll examine potential applications, including signature detection of arson, housing demolition, and indiscriminate air strikes. Our talk will also cover some technical challenges posed by the lack of training data and the complexity of detecting relevant abuse signatures, as well as consider the potential utility of synthetic training data 3D modeling.  Back
 
Topics:
AI Application Deployment and Inference
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9947
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Abstract:
How do you accelerate innovation and deliver faster time-to-value for your AI initiative, while ensuring enterprise-grade security and high performance? How do you provide easy access to the tools and data your data science teams need for large-scale ...Read More
Abstract:
How do you accelerate innovation and deliver faster time-to-value for your AI initiative, while ensuring enterprise-grade security and high performance? How do you provide easy access to the tools and data your data science teams need for large-scale distributed ML/DL with greater agility for rapid prototyping and iteration? We'll discuss practical examples and lessons learned from GPU-Accelerated ML/DL use cases in financial services, healthcare, and other industries. Learn how to quickly deploy containerized multi-node environments for TensorFlow and other ML DL tools in a multi-tenant architecture with a shared pool of resources using GPUs.  Back
 
Topics:
AI Application Deployment and Inference, AI and DL Business Track (high level)
Type:
Sponsored Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9982
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Abstract:
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
Abstract:

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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Topics:
AI Application Deployment and Inference, AI and DL Business Track (high level), Data Center and Cloud Infrastructure, AI for Business, HPC and Supercomputing
Type:
Panel
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8194
Streaming:
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Tools and Libraries, Data Center and Cloud Infrastructure
Type:
Tutorial
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8495
Streaming:
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, AI and DL Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8780
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Abstract:
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.
 
Topics:
AI Application Deployment and Inference, AI and DL Research, HPC and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8996
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S81049
Streaming:
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, NVIDIA Inception Program, Intelligent Video Analytics and Smart Cities, Deep Learning and AI Frameworks, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8584
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, AI for Gaming
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8732
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Speech and Language Processing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8968
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Advanced AI Learning Techniques
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8971
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S81046
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Astronomy and Astrophysics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8667
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Deep Learning and AI Frameworks
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S81009
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Industrial Inspection
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8944
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S81048
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Video and Image Processing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8601
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Deep Learning and AI Frameworks
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8508
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Industrial Inspection
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8911
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Accelerated Analytics, Astronomy and Astrophysics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8222
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Abstract:
A wide area and city surveillance system solution for running real-time video analytics on thousands of 1080p video streams will be presented. System hardware is an embedded computer cluster based on NVIDIA TX1/TX2 and NXP iMX6 modules. A custom ...Read More
Abstract:

A wide area and city surveillance system solution for running real-time video analytics on thousands of 1080p video streams will be presented. System hardware is an embedded computer cluster based on NVIDIA TX1/TX2 and NXP iMX6 modules. A custom designed system software manages job distribution, resulting in collection and system wide diagnostics including instantaneous voltage, power and temperature readings. System is fully integrated with a custom designed video management software, IP cameras and network video recorders. Instead of drawing algorithm results on the processed video frames, re-encoding and streaming back to the operator computer for display, only the obtained metadata is sent to the operator computer. Video management software streams video sources independently, and synchronizes decoded video frames with the corresponding metadata locally, before presenting the processed frames to the operator.

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Topics:
AI Application Deployment and Inference, Intelligent Video Analytics and Smart Cities
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8409
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Consumer Engagement and Personalization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S81016
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Consumer Engagement and Personalization, AI and DL Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8597
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Abstract:
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
Abstract:

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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Topics:
AI Application Deployment and Inference, Advanced AI Learning Techniques, AI and DL Business Track (high level), AI for Business
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8729
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Tools and Libraries, Performance Optimization, Data Center and Cloud Infrastructure
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8496
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Accelerated Analytics, AI and DL Research, AI and DL Business Track (high level)
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8303
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, NVIDIA Inception Program, Deep Learning and AI Frameworks
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8621
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Deep Learning and AI Frameworks, AI and DL Business Track (high level)
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8823
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Advanced AI Learning Techniques, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8330
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Autonomous Vehicles
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8399
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Abstract:
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
Abstract:

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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Topics:
AI Application Deployment and Inference, Deep Learning and AI Frameworks, Deep Learning and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8669
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Tools and Libraries, HPC and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8393
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Accelerated Analytics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8488
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Performance Optimization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8193
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, NVIDIA Inception Program, Cyber Security, IoT, Robotics & Autonomous Machines
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8375
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8550
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Advanced AI Learning Techniques
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8822
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Data Center and Cloud Infrastructure, AI and DL Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8196
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8173
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Industrial Inspection, IoT, Robotics & Drones, Robotics & Autonomous Machines
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8682
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Industrial Inspection, IoT, Robotics & Drones, Robotics & Autonomous Machines
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8415
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Graphics and AI, AI for Gaming, Rendering and Ray Tracing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8957
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Deep Learning and AI Frameworks, Cyber Security, Autonomous Vehicles
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8347
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Performance Optimization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8299
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Performance Optimization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8620
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Virtual Reality and Augmented Reality, Tools and Libraries, Graphics and AI, AI for Gaming
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8262
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8598
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Abstract:
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
Abstract:

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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Topics:
AI Application Deployment and Inference, Data Center and Cloud Infrastructure, Autonomous Vehicles, Autonomous Machines
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8531
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, IoT, Robotics & Drones, Robotics & Autonomous Machines
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8714
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8614
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Climate, Weather, Ocean Modeling, Computer Vision, HPC and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8816
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Speakers:
Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Data Center and Cloud Infrastructure, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8766
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Deep Learning and AI Frameworks
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8633
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, HPC and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8368
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Computational Biology and Chemistry
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8827
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Advanced AI Learning Techniques
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8635
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, AI and DL Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S81034
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Telecommunications, Deep Learning and AI Frameworks, Computer Vision, Robotics & Autonomous Machines
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8296
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, NVIDIA Inception Program, Video and Image Processing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8517
Streaming:
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Product & Building Design
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8748
Streaming:
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Industrial Inspection
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8657
Streaming:
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, Accelerated Analytics, AI and DL Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8873
Streaming:
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Abstract:
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
Abstract:
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
 
Topics:
AI Application Deployment and Inference, NVIDIA Inception Program, Accelerated Analytics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8900
Streaming:
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AI Startup
Presentation
Media
Abstract:
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
Abstract:

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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Topics:
AI Startup, Deep Learning and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7844
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Abstract:
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
Abstract:

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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Topics:
AI Startup, Healthcare and Life Sciences, Medical Imaging and Radiology
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7856
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Abstract:
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
Abstract:

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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Topics:
AI Startup, Deep Learning and AI, Finance
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7864
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AI and DL Business Track (high level)
Presentation
Media
Abstract:
Hundreds of talks and competing events crammed into a few days can be daunting. Get an overview of GTC's programs and events and how to make best use of them from Greg Estes, NVIDIA's VP of developer programs. Addressing both first-timer ...Read More
Abstract:

Hundreds of talks and competing events crammed into a few days can be daunting. Get an overview of GTC's programs and events and how to make best use of them from Greg Estes, NVIDIA's VP of developer programs. Addressing both first-timers and returning alums, Greg will cover how to get the most from your time here, including can't-miss talks and never-before-seen tech demos. He'll also cover NVIDIA's resources for developers, startups, and larger organizations, as well as training courses and networking opportunities.

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Topics:
AI and DL Business Track (high level)
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S91004
Streaming:
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Abstract:
A fireside chat with U.S. Rep. Jerry McNerney (D-Calif.), co-chair of the congressional AI caucus, and Ned Finkle, VP of Govt. Affairs, NVIDIA. Artificial Intelligence has become a front-and-center issue for policymakers. Legislative propos ...Read More
Abstract:

A fireside chat with U.S. Rep. Jerry McNerney (D-Calif.), co-chair of the congressional AI caucus, and Ned Finkle, VP of Govt. Affairs, NVIDIA. Artificial Intelligence has become a front-and-center issue for policymakers. Legislative proposals to encourage AI development and head off possible harms are gaining traction, and the Administration is working to build a national strategy. This fireside chat will give enterprises and researchers a first-hand look at how key Members of Congress are approaching AI, as well as what policies they're advocating for and expect.

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Topics:
AI and DL Business Track (high level)
Type:
Panel
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S91006
Streaming:
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Abstract:
Innovation is critical to every enterprise, but rarely convenient. We'll explore how business leaders approach the dual responsibility of building business growth and efficiencies through AI innovation while navigating the change cycles that every e ...Read More
Abstract:
Innovation is critical to every enterprise, but rarely convenient. We'll explore how business leaders approach the dual responsibility of building business growth and efficiencies through AI innovation while navigating the change cycles that every enterprise experiences.  Back
 
Topics:
AI and DL Business Track (high level)
Type:
Panel
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S91015
Streaming:
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Abstract:
As machine learning and deep learning techniques evolve into mainstream adoption, the architectural considerations for platforms that support large-scale production deployments of AI applications change significantly. How do you ensure IO bottlenecks ...Read More
Abstract:
As machine learning and deep learning techniques evolve into mainstream adoption, the architectural considerations for platforms that support large-scale production deployments of AI applications change significantly. How do you ensure IO bottlenecks are eliminated to keep your GPU-Powered AI rocket ship fueled with data? How do you address the issues of data gravity, data scaling, and data economics to support Petabyte-sized data sets? How do you simplify data management and minimize business risk and life cycle costs of large scale AI platforms? Well address these questions, discuss key business and architectural requirements for compute and storage, and explain how enterprises can achieve the maximum benefit from AI platforms that align with these requirements. Well also introduce the Dell, EMC, and NVIDIA solution portfolio which makes AI simple, flexible, and accessible.  Back
 
Topics:
AI and DL Business Track (high level), AI and DL Research
Type:
Sponsored Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S91016
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Abstract:
This customer panel brings together AI implementers who have deployed deep learning at scale. The discussion will focus on specific technical challenges they faced, solution design considerations, and best practices learned from implementing the ...Read More
Abstract:

This customer panel brings together AI implementers who have deployed deep learning at scale. The discussion will focus on specific technical challenges they faced, solution design considerations, and best practices learned from implementing their respective solutions.

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Topics:
AI and DL Business Track (high level), Data Center and Cloud Infrastructure, Deep Learning and AI Frameworks
Type:
Panel
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9121
Streaming:
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Abstract:
The size and complexity of problems that can be tackled with machine learning, and particularly deep learning, represents a new approach to business problem-solving. Learn to identify use cases for ML and acquire best practices to frame problems in a ...Read More
Abstract:
The size and complexity of problems that can be tackled with machine learning, and particularly deep learning, represents a new approach to business problem-solving. Learn to identify use cases for ML and acquire best practices to frame problems in a way that key stakeholders and senior management can understand and support. In our talk, we'll help delegates set the stage for delivering successful ML-based solutions to their businesses.  Back
 
Topics:
AI and DL Business Track (high level), Accelerated Data Science
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9295
Streaming:
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Abstract:
Designed for the business leader, this session is a getting started primer for deep learning in the enterprise. Through cross-industry use cases, panelists will discuss adoption considerations, developing teams, building proof-of-concepts, and measur ...Read More
Abstract:
Designed for the business leader, this session is a getting started primer for deep learning in the enterprise. Through cross-industry use cases, panelists will discuss adoption considerations, developing teams, building proof-of-concepts, and measurement.  Back
 
Topics:
AI and DL Business Track (high level)
Type:
Panel
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9937
Streaming:
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Abstract:
Artificial Intelligence has the potential to profoundly affect our world and lives. In this era of constant change, how do organizations keep up? We'll discuss the forces that drive technology forward and the technology trends, including AI, ...Read More
Abstract:

Artificial Intelligence has the potential to profoundly affect our world and lives. In this era of constant change, how do organizations keep up? We'll discuss the forces that drive technology forward and the technology trends, including AI, that can help organizations remain relevant in a world of constant transformation.

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Topics:
AI and DL Business Track (high level)
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9938
Streaming:
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Abstract:
By 2035, artificial intelligence could increase productivity by 40 percent or more. Manufacturing, healthcare, retail, and other key industries will benefit. We'll discuss how we're driving operational efficiencies within our organizations with AI ...Read More
Abstract:
By 2035, artificial intelligence could increase productivity by 40 percent or more. Manufacturing, healthcare, retail, and other key industries will benefit. We'll discuss how we're driving operational efficiencies within our organizations with AI applications, from getting started to advanced systems.  Back
 
Topics:
AI and DL Business Track (high level)
Type:
Panel
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9941
Streaming:
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Abstract:
Retailers are on the front lines of using AI as an instrument of innovation, ranging from in-store experiences to back-end efficiencies. We'll provide practical insights from retail industry leaders that outline success criteria that any industry ca ...Read More
Abstract:
Retailers are on the front lines of using AI as an instrument of innovation, ranging from in-store experiences to back-end efficiencies. We'll provide practical insights from retail industry leaders that outline success criteria that any industry can apply to its strategy, from optimization of supply chain routes to spend management.  Back
 
Topics:
AI and DL Business Track (high level)
Type:
Panel
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9942
Streaming:
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Abstract:
Data quality is a challenge all businesses face as they move forward with AI applications. Real-world data can be flawed, and attempting to create it for training and inference is sometimes too dangerous or costly to tackle. We'll discuss synthetic ...Read More
Abstract:
Data quality is a challenge all businesses face as they move forward with AI applications. Real-world data can be flawed, and attempting to create it for training and inference is sometimes too dangerous or costly to tackle. We'll discuss synthetic data, which can be created in controlled environments, even virtual ones. We'll explain why we see it as the future of data generation that will help businesses succeed.  Back
 
Topics:
AI and DL Business Track (high level)
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9943
Streaming:
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Abstract:
How has AI had an effect on the world around us? Hear the extraordinary stories showcased in the I am AI keynote video and documentary series, and learn how humans and technology are working together to solve the grand challenges of our time. ...Read More
Abstract:
How has AI had an effect on the world around us? Hear the extraordinary stories showcased in the I am AI keynote video and documentary series, and learn how humans and technology are working together to solve the grand challenges of our time.  Back
 
Topics:
AI and DL Business Track (high level)
Type:
Panel
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9944
Streaming:
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Abstract:
Healthcare has been on the frontlines of AI adoption for some time, using it to solve critical problems impacting humanity at large. What can other industries learn from healthcare AI innovation, in terms of applications and benchmarks? What insights ...Read More
Abstract:
Healthcare has been on the frontlines of AI adoption for some time, using it to solve critical problems impacting humanity at large. What can other industries learn from healthcare AI innovation, in terms of applications and benchmarks? What insights can be shared about cross-industry concerns such as privacy and security? How will healthcare further evolve now that data-centric models are driving new models?  Back
 
Topics:
AI and DL Business Track (high level)
Type:
Panel
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9989
Streaming:
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Abstract:
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
Abstract:

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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Topics:
AI and DL Business Track (high level), AI for Business
Type:
Panel
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8953
Streaming:
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Abstract:
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
Abstract:

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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Topics:
AI and DL Business Track (high level), AI for Business
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8984
Streaming:
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Abstract:
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
Abstract:
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
 
Topics:
AI and DL Business Track (high level), Telecommunications, Speech and Language Processing, NVIDIA Inception Program
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8274
Streaming:
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Abstract:
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
Abstract:
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
 
Topics:
AI and DL Business Track (high level), AI and DL Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8939
Streaming:
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Abstract:
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
Abstract:
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
 
Topics:
AI and DL Business Track (high level)
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8954
Streaming:
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Abstract:
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
Abstract:
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
 
Topics:
AI and DL Business Track (high level)
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8952
Streaming:
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Abstract:
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
Abstract:
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
 
Topics:
AI and DL Business Track (high level), NVIDIA Inception Program
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8989
Streaming:
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Abstract:
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
Abstract:

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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Topics:
AI and DL Business Track (high level), AI for Business
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8983
Streaming:
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Abstract:
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
Abstract:
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
 
Topics:
AI and DL Business Track (high level), NVIDIA Inception Program
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8997
Streaming:
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Abstract:
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
Abstract:
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
 
Topics:
AI and DL Business Track (high level)
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8976
Streaming:
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Abstract:
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
Abstract:
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
 
Topics:
AI and DL Business Track (high level), Virtual Reality and Augmented Reality, Consumer Engagement and Personalization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8131
Streaming:
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Abstract:
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
Abstract:
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
 
Topics:
AI and DL Business Track (high level), Predictive Analytics for Retail, Consumer Engagement and Personalization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8265
Streaming:
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Abstract:
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
Abstract:
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
 
Topics:
AI and DL Business Track (high level), GIS, AI and DL Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S81028
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Abstract:
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
Abstract:
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
 
Topics:
AI and DL Business Track (high level)
Type:
Talk
Event:
GTC Washington D.C.
Year:
2017
Session ID:
DC7265
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AI and DL Research
Presentation
Media
Abstract:
Learn how to design GPU-Based systems for different application scenarios. We'll explain how to design data centers for different scales, application scenarios, and standards for enterprises and hyperscalers. We'll cover AI training and inference a ...Read More
Abstract:
Learn how to design GPU-Based systems for different application scenarios. We'll explain how to design data centers for different scales, application scenarios, and standards for enterprises and hyperscalers. We'll cover AI training and inference applications and edge computing for OCP and ODCC standard data centers. We'll discuss the challenges involved and share our experience designing a GPU platform for data centers. We'll also explore problems attendees are facing and see how we can work together to solve them.  Back
 
Topics:
AI and DL Research, HPC and AI
Type:
Sponsored Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S91013
Streaming:
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Abstract:
Learn about the latest research on improvements to text-to-speech models and workflows using Tacotron2 and Waveglow produced by NVIDIA's applied deep learning research team. In partnership with our deep learning algorithm development team, learn mor ...Read More
Abstract:
Learn about the latest research on improvements to text-to-speech models and workflows using Tacotron2 and Waveglow produced by NVIDIA's applied deep learning research team. In partnership with our deep learning algorithm development team, learn more about how Tensor Cores have made fast mixed-precision training and faster than real-time inference performance available. We'll also be showing a demo, reviewing accuracy, and performance metrics through the open source implementation available on GitHub.  Back
 
Topics:
AI and DL Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S91022
Streaming:
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Abstract:
In order to obtain peak performance and energy efficiency on modern deep learning architectures, such as GPUs and TPUs, it is critical to use half precision arithmetic. Compared to single precision, half precision reduces memory traffic, allowing 2x ...Read More
Abstract:
In order to obtain peak performance and energy efficiency on modern deep learning architectures, such as GPUs and TPUs, it is critical to use half precision arithmetic. Compared to single precision, half precision reduces memory traffic, allowing 2x better use of the available DRAM bandwidth. Smaller memory footprints for half precision layer activations also allow larger batch sizes and deeper network architectures to fit in the accelerator's memory during training. Finally, architectural features, such as Volta's Tensor Cores, boost the raw math throughput of half precision operations by up to 8x compared to single precision. We describe two new streamlined implementations of mixed-precision training being built into TensorFlow. The first is provided through extensions to the tf.keras API and will be available in the upcoming months. The second is based on a Grappler graph optimization pass and will work with TF 1.x graph-based models as well as future TensorFlow 2.0 models that make use of tf.function decorators. Each method is enabled using a one or two line tweak to the training script. Empirical results show that result accuracy matches that of a model trained in single-precision, while training speedup is similar to what can be achieved with hand-coded mixed precision strategies.  Back
 
Topics:
AI and DL Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S91029
Streaming:
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Abstract:
GPUs are powering us into the future. Through research, we will be able to see where this technology takes us next. This session serves as a catalyst for the advancement of an array of innovations that come from universities, research labs, an ...Read More
Abstract:
GPUs are powering us into the future. Through research, we will be able to see where this technology takes us next. This session serves as a catalyst for the advancement of an array of innovations that come from universities, research labs, and industry. Join us in hearing how this year’s Top 5 Poster finalists were inspired to start their research, what surprised them, and where they want to see it go next.
 
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Topics:
AI and DL Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S91036
Streaming:
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Abstract:
I will introduce a game developed at Johns Hopkins University/Applied Physics Laboratory called Reconnaissance Blind Chess (RBC), a chess variant where the players do not see their opponent's moves, but they can gain information about t ...Read More
Abstract:

I will introduce a game developed at Johns Hopkins University/Applied Physics Laboratory called Reconnaissance Blind Chess (RBC), a chess variant where the players do not see their opponent's moves, but they can gain information about the ground-truth board position through the use of an (imperfect) sensor.  RBC incorporates key aspects of active sensing and planning: players have to decide where to sense, use the information gained through sensing to update their board estimates, and use that world model to decide where to move.  Thus, just as chess and go have been challenge problems for decision making with complete information, RBC is intended to be a common challenge problem for decision making under uncertainty.  After motivating the game concept and its relationship to other chess variants, I will describe the current rules of RBC as well as other potential rulesets, give a short introduction to the game implementation and bot API, and discuss some of our initial research on the complexity of RBC as well as bot algorithm

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Topics:
AI and DL Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S91044
Streaming:
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Abstract:
We'll discuss how using neural networks and deep reinforcement learning can be used to design potential drug candidates. The pharmaceutical industry is crying out for a revolution in thinking and practice; the traditional methods of drug discovery a ...Read More
Abstract:
We'll discuss how using neural networks and deep reinforcement learning can be used to design potential drug candidates. The pharmaceutical industry is crying out for a revolution in thinking and practice; the traditional methods of drug discovery and development are no longer working well. To continue to prosper, either R&D costs must be lowered or the rate of discovery for the new drugs must be drastically increased. We'll talk about how AI offers an opportunity to transform the field and dramatically accelerate the design of new drug candidates. The unique proposition of AI is the ability to learn directly from past experience and capture hidden dependences from both structured and unstructured data. As the chemical data is getting bigger, deep learning methods coupled with fast GPU computations make it possible to process vast amounts of information to find clinically relevant relationships and overcome drug discovery bottlenecks.  Back
 
Topics:
AI and DL Research, Computational Biology and Chemistry, AI in Healthcare
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9110
Streaming:
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Abstract:
We'll introduce Data2Vis, a neural translation model for automatically generating visualizations from given datasets. We formulate visualization generation as a sequence-to-sequence translation problem in which data is mapped to visualization specif ...Read More
Abstract:
We'll introduce Data2Vis, a neural translation model for automatically generating visualizations from given datasets. We formulate visualization generation as a sequence-to-sequence translation problem in which data is mapped to visualization specifications in a declarative language. We'll discuss how we train a multilayered attention-based encoder-decoder model on a corpus of visualization specifications. Qualitative results show that the model learns the vocabulary and syntax for valid visualization specifications, appropriate transformations, and how to use common data-selection patterns occurring within data visualizations. We'll describe how Data2Vis generates high-quality visualizations comparable to manual efforts in a fraction of the time, and how it has the potential to learn more complex visualization strategies at scale. We will also provide guidance on training such a model using the Cloudera Datascience Workbench and explore uses for Data2Vis within visualization tools.  Back
 
Topics:
AI and DL Research, AI Application Deployment and Inference, Programming Languages
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9140
Streaming:
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Abstract:
We'll discuss an implementation of GPU convolution that favors coalesced accesses without requiring prior data transformations. Convolutions are the core operation of deep learning applications based on convolutional neural networks. Current ...Read More
Abstract:

We'll discuss an implementation of GPU convolution that favors coalesced accesses without requiring prior data transformations. Convolutions are the core operation of deep learning applications based on convolutional neural networks. Current GPU architectures are typically used for training deep CNNs, but some state-of-the-art implementations are inefficient for some commonly used network configurations. We'll discuss experiments that used our new implementation, which yielded notable performance improvements including up to 2.29X speedups in a wide range of common CNN configurations.

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Topics:
AI and DL Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9218
Streaming:
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Abstract:
Learning long-term dependencies in extended temporal sequences requires credit assignment to events far in the past. The most common method for training recurrent neural networks, backpropagation through time, requires credit information to be propag ...Read More
Abstract:
Learning long-term dependencies in extended temporal sequences requires credit assignment to events far in the past. The most common method for training recurrent neural networks, backpropagation through time, requires credit information to be propagated backwards through every single step of the forward computation, potentially over thousands or millions of time steps. We'll describe how this becomes computationally expensive or even infeasible when used with long sequences. Although biological brains are unlikely to perform such detailed reverse replay over very long sequences of internal states, humans often reminded of past memories or mental states associated with their current mental states. We'll discuss the hypothesis that such memory associations between past and present could be used for credit assignment through arbitrarily long sequences, propagating the credit assigned to the current state to the associated past state.  Back
 
Topics:
AI and DL Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9251
Streaming:
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Abstract:
Learn how to apply deep learning technologies for building robust and scalable dialogue systems with deeper understanding of the classic pipelines and final out more about the benchmark of models of prior work. We'll give an overview of dialogue res ...Read More
Abstract:
Learn how to apply deep learning technologies for building robust and scalable dialogue systems with deeper understanding of the classic pipelines and final out more about the benchmark of models of prior work. We'll give an overview of dialogue research and details state-of-the-art end-to-end neural dialogue systems for both task-oriented and social chit-chat conversations.  Back
 
Topics:
AI and DL Research, Speech and Language Processing
Type:
Tutorial
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9276
Streaming:
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Abstract:
We'll discuss Alibaba's PAI tensor accelerator and optimizer (PAI-Tao), an elaborately implemented and optimized AI engine for deep learning training and inference tasks. PAI-Tao is designed with a data-driven and compiler-oriented approach. It per ...Read More
Abstract:
We'll discuss Alibaba's PAI tensor accelerator and optimizer (PAI-Tao), an elaborately implemented and optimized AI engine for deep learning training and inference tasks. PAI-Tao is designed with a data-driven and compiler-oriented approach. It periodically collects online running statistics to provide insights for optimization and uses collected statistics to help drive the real optimization work. We'll outline how PAI-Tao's compiler-oriented design can better accommodate diversified and fast-changing AI workloads.  Back
 
Topics:
AI and DL Research, Accelerated Data Science
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9280
Streaming:
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Abstract:
Training and tuning models with lengthy training cycles like those in deep learning can be extremely expensive and may sometimes involve techniques that degrade performance. We'll explore recent research on optimization strategies to efficiently tun ...Read More
Abstract:
Training and tuning models with lengthy training cycles like those in deep learning can be extremely expensive and may sometimes involve techniques that degrade performance. We'll explore recent research on optimization strategies to efficiently tune these types of deep learning models. We will provide benchmarks and comparisons to other popular methods for optimizing the models, and we'll recommend valuable areas for further applied research.  Back
 
Topics:
AI and DL Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9313
Streaming:
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Abstract:
We will discuss a deep learning-based method for improving the quality of 3D reconstruction performed by time-of-flight cameras. Scene motion, multiple reflections, and sensor noise introduce artifacts in the depth reconstruction performed by these s ...Read More
Abstract:
We will discuss a deep learning-based method for improving the quality of 3D reconstruction performed by time-of-flight cameras. Scene motion, multiple reflections, and sensor noise introduce artifacts in the depth reconstruction performed by these sensors. We'll explain our proposed two-stage, deep-learning approach to address all of these sources of artifacts simultaneously. We'll also introduce FLAT, a synthetic dataset of 2000 ToF measurements that capture all of these nonidealities and can be used to simulate different hardware. Using the Kinect camera as a baseline, we show improved reconstruction errors on simulated and real data, as compared with state-of-the-art methods.  Back
 
Topics:
AI and DL Research, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9318
Streaming:
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Abstract:
In recent year, there is growing interest in advancing AI in the reasoning field, as reasoning is one of the main abilities associated with intelligence. Deep learning performs exceptionally well at pattern recognition and in recent times, it is adva ...Read More
Abstract:
In recent year, there is growing interest in advancing AI in the reasoning field, as reasoning is one of the main abilities associated with intelligence. Deep learning performs exceptionally well at pattern recognition and in recent times, it is advancing into the reasoning field, from relational networks for question answering and transparency-by-design networks for visual reasoning. In this talk, we will share an alternate and complementary paradigm for performing reasoning with a type theoretic approach.  Back
 
Topics:
AI and DL Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9383
Streaming:
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Abstract:
Learn how to achieve real-world speedup of neural networks using structural sparsity. Structural sparsity reduces the number of weights and computations in a way that's suitable for hardware acceleration. Over-parameterized neural networks waste mem ...Read More
Abstract:
Learn how to achieve real-world speedup of neural networks using structural sparsity. Structural sparsity reduces the number of weights and computations in a way that's suitable for hardware acceleration. Over-parameterized neural networks waste memory and energy. Techniques like pruning or factorization can alleviate this during inference but they often increase training time, and achieving real-world speedups remains difficult. We'll explain how biology-inspired techniques can reduce the number of weights from quadratic to linear in the number of neurons. Compared to fully connected neural networks, these structurally sparse neural networks achieve large speedups during both training and inference, while maintaining or even improving model accuracy. We'll discuss hardware considerations and results for feed-forward and recurrent networks.  Back
 
Topics:
AI and DL Research, Algorithms and Numerical Techniques
Type:
Tutorial
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9389
Streaming:
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Abstract:
MATLAB's deep learning, visualization, and C++/CUDA code generation technology make it a uniquely complete solution for your entire AI workflow. In MATLAB, you can easily manage data, perform complex image and signal processing, prototype and train ...Read More
Abstract:
MATLAB's deep learning, visualization, and C++/CUDA code generation technology make it a uniquely complete solution for your entire AI workflow. In MATLAB, you can easily manage data, perform complex image and signal processing, prototype and train deep networks, and deploy to your desktop, embedded or cloud environments. Using GPU Coder technology MATLAB generates CUDA kernels that optimize loops and memory access, and C++ that leverages cuDNN and TensorRT, providing the fastest deep network inference of any framework. With MATLAB's NVIDIA docker container available through the NVIDIA GPU Cloud, you can now easily access all this AI power, deploy it in your cloud or DGX environment, and get up and running in seconds. In this presentation we will demonstrate a complete end-to-end workflow that starts from 'docker run', prototypes and trains a network on a multi-GPU machine in the cloud, and ends with a highly optimized inference engine to deploy to data centers, clouds, and embedded devices.  Back
 
Topics:
AI and DL Research, Data Center and Cloud Infrastructure
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9469
Streaming:
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Abstract:
New to ML? Want to get started using TensorFlow together with GPUs? We will cover how you should use TensorFlow APIs to define and train your models, and discuss best practices for distributing the training workloads to multiple GPUs. We will also lo ...Read More
Abstract:
New to ML? Want to get started using TensorFlow together with GPUs? We will cover how you should use TensorFlow APIs to define and train your models, and discuss best practices for distributing the training workloads to multiple GPUs. We will also look at why GPUs are so great for machine learning workloads. This talk is appropriate for beginners who want to learn what TensorFlow can do.  Back
 
Topics:
AI and DL Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9517
Streaming:
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Abstract:
Learn about object detection and how GPUs are boosting the field in the PyTorch framework. As part of our talk, we'll discuss GPU implementation, including the efficiency-accuracy tradeoff and cluster deployment. We'll also delve into the latest ob ...Read More
Abstract:
Learn about object detection and how GPUs are boosting the field in the PyTorch framework. As part of our talk, we'll discuss GPU implementation, including the efficiency-accuracy tradeoff and cluster deployment. We'll also delve into the latest object-detection research.  Back
 
Topics:
AI and DL Research, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9551
Streaming:
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Abstract:
Learn how to quickly build robust deep neural networks for visual-recognition tasks using information generated directly from the human brain. We will present a novel active learning framework that combines fast image presentation, real-time brainwav ...Read More
Abstract:
Learn how to quickly build robust deep neural networks for visual-recognition tasks using information generated directly from the human brain. We will present a novel active learning framework that combines fast image presentation, real-time brainwave classification, and the use of classification score for optimizing the loss function. We'll share examples of using the proposed framework on GPUs to train neural networks and show that our solution provides faster convergence and higher performance than traditional methods.  Back
 
Topics:
AI and DL Research, Accelerated Data Science
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9554
Streaming:
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Abstract:
We'll introduce a method for constructing an accurate prediction model from limited data in machine learning, one of the most important tasks in machine learning. We'll discuss unsupervised domain adaptation for open set data and a visual question- ...Read More
Abstract:
We'll introduce a method for constructing an accurate prediction model from limited data in machine learning, one of the most important tasks in machine learning. We'll discuss unsupervised domain adaptation for open set data and a visual question-generation method to acquire knowledge of unknown object categories.  Back
 
Topics:
AI and DL Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9598
Streaming:
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Abstract:
We'll describe joint NVIDIA-Amazon work to build AI assistive tools for the caretakers of the Amazon Biospheres. We train deep autoencoders on time-series sensor streams for monitoring anomalies in the micro-climate. Our talk will cover how we deplo ...Read More
Abstract:
We'll describe joint NVIDIA-Amazon work to build AI assistive tools for the caretakers of the Amazon Biospheres. We train deep autoencoders on time-series sensor streams for monitoring anomalies in the micro-climate. Our talk will cover how we deploy convolutional architectures for tracking plant stress levels using time-lapse vision models. We'll outline how we try to use best practices for edge-to-cloud AI and how we built a workflow to train models on EC2.P3 instances (NVIDIA Tesla V100 GPUs on AWS SageMaker). We'll also discuss how we optimize models for inference using TensorRT and subsequently deploy those models on NVIDIA Jetson TX2s for the biosphere.  Back
 
Topics:
AI and DL Research, Intelligent Machines and IoT, AI Application Deployment and Inference
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9627
Streaming:
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Abstract:
We'll introduce several deep angular/hyperpherical learning frameworks and their applications in computer vision. Deep angular/hyperspherical learning provides state-of-the-art performance for general image classification and face recognition proble ...Read More
Abstract:
We'll introduce several deep angular/hyperpherical learning frameworks and their applications in computer vision. Deep angular/hyperspherical learning provides state-of-the-art performance for general image classification and face recognition problems. We'll talk about the motivation behind this type of learning and introduce some relevant variants under this framework. Deep hyperspherical learning has diverse applications in computer vision, and can also be used for learning neural network architectures and improving neural network generalization. We'll also discuss a few open problems in this framework and talk about some potential applications.  Back
 
Topics:
AI and DL Research, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9631
Streaming:
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Abstract:
Learn why deep learning scales so well and how to apply it to important open problems. Deep learning has enabled rapid progress in diverse problems in vision, speech, and beyond. Driving this progress are breakthroughs in algorithms that can harness ...Read More
Abstract:
Learn why deep learning scales so well and how to apply it to important open problems. Deep learning has enabled rapid progress in diverse problems in vision, speech, and beyond. Driving this progress are breakthroughs in algorithms that can harness massive datasets and powerful compute accelerators like GPUs. We'll combine theoretical and experiment insights to help explain why deep learning scales predictably with bigger datasets and faster computers. We'll also show how some problems are relatively easier than others and how to tell the difference. Learn about examples of open problems that cannot be solved by individual computers, but are within reach of the largest machines in the world. We'll also make the case for optimizing data centers to run AI workloads. Finally, we'll outline a high-level architecture for an AI datacenter, and leave you with powerful tools to reach beyond human accuracy to confront some of the hardest open problems in computing.  Back
 
Topics:
AI and DL Research, HPC and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9643
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Abstract:
Academic design of deep neural networks has historically focused on maximizing accuracy at any cost. However, many practical applications have real-world constraints such as model size, computational complexity (FLOPs), or inference latency, as well ...Read More
Abstract:
Academic design of deep neural networks has historically focused on maximizing accuracy at any cost. However, many practical applications have real-world constraints such as model size, computational complexity (FLOPs), or inference latency, as well as physical hardware performance, that need to be considered. We'll discuss our MorphNet solution, an approach to automate the design of neural nets with constraint-specific and hardware-specific tradeoffs while being lightweight and scalable to large data sets. We show how MorphNet can be used to design neural nets that reduce model size, FLOP count, or inference latency with the same accuracy across different domains such as ImageNet, OCR, and AudioSet. Finally, we show how MorphNet designs different architectures when optimizing for P100 and V100 platforms.  Back
 
Topics:
AI and DL Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9645
Streaming:
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Abstract:
In this talk, I'll discuss several semi-supervised learning applications from our recent work in applied deep learning research at NVIDIA. I'll first discuss video translation, which renders new scenes using models learned from real-world videos. W ...Read More
Abstract:
In this talk, I'll discuss several semi-supervised learning applications from our recent work in applied deep learning research at NVIDIA. I'll first discuss video translation, which renders new scenes using models learned from real-world videos. We take real world videos, analyze them using existing computer vision techniques such as pose estimation or semantic segmentation, and then train generative models to invert these poses or segmentations back to videos. In deployment, we then render novel sketches using these models. I'll then discuss work on large-scale language modeling, where a model trained to predict text, piece by piece, on a large dataset is then finetuned with small amounts of labeled data to solve problems like emotion classification. Finally, I'll discuss WaveGlow, our flow-based generative model for the vocoder stage of speech synthesis, that combines a simple log-likelihood based training procedure with very fast and efficient inference. Because semi-supervised learning allows us to try tackling problems where large amounts of labels would be prohibitively expensive to create, it opens the scope of problems to which we can apply machine learning.  Back
 
Topics:
AI and DL Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9686
Streaming:
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Abstract:
We'll talk about how we're incorporating physics into deep learning algorithms. Standard deep learning algorithms are based on a function-fitting approach that does not exploit any domain knowledge or constraints. This makes them unsuitable for app ...Read More
Abstract:
We'll talk about how we're incorporating physics into deep learning algorithms. Standard deep learning algorithms are based on a function-fitting approach that does not exploit any domain knowledge or constraints. This makes them unsuitable for applications like robotics that require safety or stability guarantees. These algorithms also require large amounts of labeled data, which is not readily available. We'll discuss how we're overcoming these limitations by infusing physics into deep learning algorithms, and how we're applying this to stable landing of quadrotor drones. We've developed a robust deep learning-based nonlinear controller called Neural-Lander, which learns ground-effect aerodynamic forces that are hard to model. We'll also touch on how Neural-Lander can land significantly faster while maintaining stability.  Back
 
Topics:
AI and DL Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9732
Streaming:
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Abstract:
Learn about tensors, higher-order extensions of matrices that can incorporate multiple modalities and encode higher-order relationships in data. After an introduction to tensor methods, we will discuss which tensor methods can be used in deep learnin ...Read More
Abstract:
Learn about tensors, higher-order extensions of matrices that can incorporate multiple modalities and encode higher-order relationships in data. After an introduction to tensor methods, we will discuss which tensor methods can be used in deep learning and in probabilistic modeling. We'll show how tensor contractions, which are extensions of matrix products, provide high rates of compression in a variety of neural network models. We'll also demonstrate the use of tensors for document categorization at scale through probabilistic topic models. These are available in a python library called Tensorly that provides a high-level API for tensor methods and deep tensorized architectures.  Back
 
Topics:
AI and DL Research
Type:
Tutorial
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9733
Streaming:
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Abstract:
We'll present our study on GPU optimization for deep learning with limited computational resources and share our tips and tricks for building a state-of-the-art Visual Question Answering (VQA) system. Learn about technical implementations of deep le ...Read More
Abstract:
We'll present our study on GPU optimization for deep learning with limited computational resources and share our tips and tricks for building a state-of-the-art Visual Question Answering (VQA) system. Learn about technical implementations of deep learning algorithms with GPU hardware utilization, including delayed updates and mixed-precision training, to deal with limited hardware resources while reduce training time and memory usage. We'll describe our experience designing a winning architecture for the VQA Challenge 2018 by applying deep learning tactics such as multi-level multi-modal fusion, parameter-interaction learning, and end-to-end optimization. Our techniques are all heavy computing tasks, so GPU programming plays an important role in advancing our work. We'll also provide convincing empirical proofs and a practical demonstration of a VQA application.  Back
 
Topics:
AI and DL Research, AI Application Deployment and Inference
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9824
Streaming:
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Abstract:
We'll discuss learning to synthesize object instances such as a person or car in both 2D and 3D scenes. We will introduce our work we presented at NeurIPS 2018 on context-aware synthesis and placement of object instances. We propose a generative mod ...Read More
Abstract:
We'll discuss learning to synthesize object instances such as a person or car in both 2D and 3D scenes. We will introduce our work we presented at NeurIPS 2018 on context-aware synthesis and placement of object instances. We propose a generative model that learns to generate and insert an object instance into an image in a semantically coherent manner. In particular, we represent object instances using masks and learn to insert them into semantic label maps of images. Our talk will also cover our recent work around putting humans in a scene and learning affordance in 3D indoor environments. This extends the learning of context from 2D to 3D scenes in which the synthesized objects are semantically coherent and geometrically correct. We'll show that both projects add technical insights and have potential applications in content creation.  Back
 
Topics:
AI and DL Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9959
Streaming:
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Abstract:
Join a special presentation from our 2018-2019 Graduate Fellowship recipients to learn what's next from the world of research and academia. Sponsored projects involve a variety of technical challenges, including topics such as 3D scene understanding ...Read More
Abstract:
Join a special presentation from our 2018-2019 Graduate Fellowship recipients to learn what's next from the world of research and academia. Sponsored projects involve a variety of technical challenges, including topics such as 3D scene understanding, new programming models for tensor computations, HPC physics simulations for astrophysics, deep learning algorithms for AI natural language learning, and cancer diagnosis. We believe that theses students will lead the future in our industry and we're proud to support the 2018-2019 NVIDIA Graduate Fellows. For more information on the NVIDIA Graduate Fellowship program, visit www.nvidia.com/en-us/research/graduate-fellowships.  Back
 
Topics:
AI and DL Research, Virtual Reality and Augmented Reality, Graphics and AI, Computational Biology and Chemistry, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9976
Streaming:
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Abstract:
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
Abstract:
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
 
Topics:
AI and DL Research, Algorithms and Numerical Techniques
Type:
Tutorial
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S81012
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Abstract:
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
Abstract:
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
 
Topics:
AI and DL Research, Data Center and Cloud Infrastructure
Type:
Tutorial
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8568
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Abstract:
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
Abstract:
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
 
Topics:
AI and DL Research, Accelerated Analytics, HPC and Supercomputing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8668
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Abstract:
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
Abstract:
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
 
Topics:
AI and DL Research, Telecommunications
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8791
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Abstract:
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
Abstract:
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
 
Topics:
AI and DL Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8200
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Abstract:
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
Abstract:
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
 
Topics:
AI and DL Research, Computational Physics
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8826
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Abstract:
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
Abstract:
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
 
Topics:
AI and DL Research, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8191
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Abstract:
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
Abstract:
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
 
Topics:
AI and DL Research, Deep Learning and AI Frameworks, Data Center and Cloud Infrastructure
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8497
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Abstract:
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
Abstract:
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
 
Topics:
AI and DL Research, Deep Learning and AI Frameworks
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8977
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Abstract:
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
Abstract:

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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Topics:
AI and DL Research, Telecommunications, Federal
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8267
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