School of Electrical and Computer Engineering Seminar
Title. Challenges and Opportunities in Deep Learning
Speaker: Greg Diamos (Baidu’s Silicon Valley AI Lab (SVAIL))
Date/Time/Location: Thursday, February 23rd from 11:00 – 12:00 PM in Van Leer C341
Just this year, deep learning has fueled significant progress in computer vision, speech recognition, and natural language processing. We have seen a computer defeat the world champion in Go with help from deep learning, and a single deep learning algorithm learn to recognize two vastly different languages, English and Mandarin. At Baidu, we think that this is just the beginning, and high performance computing is poised to help.
It turns out that deep learning is compute limited, even on the fastest machines that we can build. This talk will provide empirical evidence from our Deep Speech work that application level performance (e.g. recognition accuracy) scales with data and compute, transforming some hard AI problems into problems of computational scale. It will describe the performance characteristics of Baidu’s deep learning workloads in detail, focusing on the recurrent neural networks used in Deep Speech as a case study. It will cover challenges to further improving performance, and outline a plan of attack for tearing down the remaining obstacles standing in the way of strong scaling deep learning to the largest machines in the world.
The talk will conclude with open problems across the entire hardware/software stack, from electrons to AI frameworks, and suggest directions for future work.
Bio: Greg Diamos leads computer systems research at Baidu’s Silicon Valley AI Lab (SVAIL). Previously he was on the research team at NVIDIA, where he contributed to the design of the Volta GPU. Greg holds a PhD from the Georgia Institute of Technology, where he contributed to the development of the GPU-Ocelot dynamic compiler, which targeted CPUs and GPUs from the same program representation. His PhD thesis developed new execution models for heterogeneous processors.
Pizza and refreshments will be served!
For additional information please contact: Professor Sudhakar Yalamanchili (email@example.com) or Bev Scheerer (Beverly.firstname.lastname@example.org)