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GTC JAPAN 9 月 13-14 日開催

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MICHAEL FRUMKIN

NVIDIA, Sr. Compute Architect

Capture Sparsity in DL Applications

We'll present a new technique for improving efficiency of inference and training in Deep Learning in the presence of sparse workloads. We'll start with a brief overview of applications of sparse linear algebra in engineering and data analysis. Then analyze the presence of sparsity in both the training and inference phases of Deep Learning. To exploit this sparsity, we present our method of improving memory locality of sparse applications. We'll establish lower and upper bounds for sparse matrix operations and crossover with dense matrix operations. We will demonstrate how to minimize memory traffic by tiling matrix operations, efficient use of L2, L1, and SMEM. We will conclude with performance comparison of our method with existing techniques on some real pruned weight matrices from GoogLeNet and OpenNMT's multiway translation network. This is joint work of Michael Frumkin, Jeff Pool and Lung Sheng Chien.

ABOUT THE SPEAKER: Michael Frumkin has a Ph.D. in computer sciences from the Russian Academy of Sciences and a MS.c. in mathematics from The Moscow State University. He has published more than 70 papers in Computer Sciences and Mathematics. Michael is a senior compute Architect at NVIDIA. Previous companies he has worked for include Google, Intel, NASA, KLA-Tencor, and The Scripps Research Institute.

MARK HARRIS

NVIDIA, Chief Technologist, GPU Computing Software

CUDA [Version] and Beyond

CUDA is NVIDIA's parallel computing platform and programming model. You'll learn about new programming model enhancements and performance improvements in the latest release of CUDA, preview upcoming GPU programming technology, and gain insight into the philosophy driving the development of CUDA and how it will take advantage of current and future GPUs. You'll also learn about NVIDIA's vision for CUDA and the challenges for the future of parallel software development.

ABOUT THE SPEAKER: Mark Harris is chief technologist for GPU computing software at NVIDIA. Mark has 15 years of experience developing software for GPUs, ranging from graphics and games to physically based simulation, parallel algorithms, and high performance computing. He has been using GPUs for general-purpose computing since before they even supported floating point arithmetic. While a Ph.D. student at the University of North Carolina, Mark recognized this nascent trend and coined a name for it: GPGPU (general-purpose computing on graphics processing units), and started GPGPU.org to provide a forum for those working in the field to share and discuss their work.

DANNY ATSMON

COGNATA, CEO

Deep Learning Autonomous Driving Simulation

Realistic automotive simulation platforms, where virtual cars travel virtual roads in virtual cities in remarkably true-to-life conditions, will be a vital part of developing and testing autonomous vehicles. The technology behind the Cognata simulation engine is heavily based on deep learning, computer vision, and other advanced AI methods. We'll present a cloud-based simulation engine, and discuss how it works and how to develop with it.

ABOUT THE SPEAKER: Danny Atsmon, an expert in advanced driver-assistance systems (ADAS) and deep learning, is the CEO at Cognata Ltd. He has been in the business of launching high-tech products for more than 20 years. Before joining Cognata, Danny served as HARMAN's (now Samsung) director of ADAS and senior director of machine learning. He co-founded two startups, Picitup and iOnRoad, the latter of which uses a phone's native camera to detect vehicles in front of a car. Danny also holds several patents and has won many top industry awards, including the CES award, CTIA award, Microsoft Think Next, and Qualcomm QPrize. Danny has a Bachelor of Science in physics from Tel Aviv University, and is a graduate of the Israeli Defense Forces Haman Talpiot program, where he served in the elite Unit 8200.

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GTC JAPAN 9 月 13-14 日 開催