SEARCH SESSIONS

Search All
 
Refine Results:
 
Year(s)

SOCIAL MEDIA

EMAIL SUBSCRIPTION

 
 

GTC On-Demand

Presentation
Media
Abstract:

Learn how to train and employ state-of-the-art object localization in a real-time safety application. In Petawatt laser systems, firing at 10Hz, suddenly appearing scatterers can damage components. Damage(-spreading) can be avoided by suspending operation immediately upon occurrence of such an event.

We present our approach for the automatic detection of critical failure states from intensity profiles of the laser beam. In order to minimize the rate of false alarms, which would reduce productivity or even render our system useless, we refrain from general anomaly detection and instead detect known error patterns. In this talk we present how we fitted the You Look Only Once(YOLO) approach, which is suited to low-latency object detection, to our problem and how we adapted the required multi-step training protocol to the available experimental data.

Learn how to train and employ state-of-the-art object localization in a real-time safety application. In Petawatt laser systems, firing at 10Hz, suddenly appearing scatterers can damage components. Damage(-spreading) can be avoided by suspending operation immediately upon occurrence of such an event.

We present our approach for the automatic detection of critical failure states from intensity profiles of the laser beam. In order to minimize the rate of false alarms, which would reduce productivity or even render our system useless, we refrain from general anomaly detection and instead detect known error patterns. In this talk we present how we fitted the You Look Only Once(YOLO) approach, which is suited to low-latency object detection, to our problem and how we adapted the required multi-step training protocol to the available experimental data.

  Back
 
Topics:
Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Europe
Year:
2018
Session ID:
E8170
Streaming:
Download:
Share:
 
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.
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
Streaming:
Share:
 
Abstract:
We'll present a method for highly efficient lattice Monte Carlo simulations with correlation-free updates. Achieving freedom from erroneous correlations requires random selection of lattice sites for updates, which must be restricted by suitable domain decomposition to create parallelism. While approaches based on caching limit the number of allowed states, the multisurface-type approach presented here allows arbitrarily complex states. The effectiveness of the method is illustrated in the fact that it allowed us to solve a long-standing dispute around surface growth under random kinetic deposition in the KPZ-universality class. The method has also been applied to Potts models and is suitable for spin-glass simulations, such as those required to test quantum annealers, like D-Wave.
We'll present a method for highly efficient lattice Monte Carlo simulations with correlation-free updates. Achieving freedom from erroneous correlations requires random selection of lattice sites for updates, which must be restricted by suitable domain decomposition to create parallelism. While approaches based on caching limit the number of allowed states, the multisurface-type approach presented here allows arbitrarily complex states. The effectiveness of the method is illustrated in the fact that it allowed us to solve a long-standing dispute around surface growth under random kinetic deposition in the KPZ-universality class. The method has also been applied to Potts models and is suitable for spin-glass simulations, such as those required to test quantum annealers, like D-Wave.  Back
 
Topics:
Computational Physics, HPC and Supercomputing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7240
Download:
Share:
 
Abstract:
Modern microscopes easily produce large data volumes (terabyte datasets) at high rate (1,000 megabytes/s is no exception) that makes using them almost impossible. Once an acquisition is started, it typically has to be stopped again as the hard drives run full. We'll share how GPUs helped us bring this nightmare to an end. We'll introduce our open-source package, called sqeazy, that is capable of compressing microscopic data at faster speeds than a hard drive can spin. We show how GPUs provided a crucial boost in this endeavor and we'll share what technical challenges we overcame interfacing with modern video encoding libraries, like libavcodec of ffmpeg. Finally, we'll discuss how NVENC provides portable performance that helps scientists to observe living developing specimens over long time spans. This may be the foundation for modern predictive biology of the 21st century. Join us for a tour on how modern media technology straight from Hollywood can boost science!
Modern microscopes easily produce large data volumes (terabyte datasets) at high rate (1,000 megabytes/s is no exception) that makes using them almost impossible. Once an acquisition is started, it typically has to be stopped again as the hard drives run full. We'll share how GPUs helped us bring this nightmare to an end. We'll introduce our open-source package, called sqeazy, that is capable of compressing microscopic data at faster speeds than a hard drive can spin. We show how GPUs provided a crucial boost in this endeavor and we'll share what technical challenges we overcame interfacing with modern video encoding libraries, like libavcodec of ffmpeg. Finally, we'll discuss how NVENC provides portable performance that helps scientists to observe living developing specimens over long time spans. This may be the foundation for modern predictive biology of the 21st century. Join us for a tour on how modern media technology straight from Hollywood can boost science!  Back
 
Topics:
Video and Image Processing, Healthcare and Life Sciences
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7247
Download:
Share: