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2019.4.22--Optimizing Device Placement in Machine Learning Workloads using Deep Reinforcement Learning
Apr 11, 2019


Optimizing   Device Placement in Machine Learning Workloads using Deep Reinforcement Learning


Prof. Baochun Li


2019-04-22 15:00:00


Room 1012, New Science & Technology Building, North Campus, Xidian University

Lecturer    Profile

Baochun Li received his   B.Engr. degree from the Department of Computer Science and Technology,   Tsinghua University, China, in 1995 and his M.S. and Ph.D. degrees from the   Department of Computer Science, University of Illinois at Urbana-Champaign,   Urbana, in 1997 and 2000.  Since 2000, he has been with the Department   of Electrical and Computer Engineering at the University of Toronto, where he   is currently a Professor. He holds the Bell Canada Endowed Chair in Computer   Engineering since August 2005. His research interests include cloud   computing, distributed systems, datacenter networking, and wireless systems.

Dr. Li has co-authored   more than 300 research papers, with a total of over 17000 citations, an   H-index of 75 and an i10-index of 233, according to Google Scholar Citations.   He was the recipient of the IEEE Communications Society Leonard G. Abraham   Award in the Field of Communications Systems in 2000. In 2009, he was a   recipient of the Multimedia Communications Best Paper Award from the IEEE   Communications Society, and a recipient of the University of Toronto McLean   Award. He is a member of ACM and a Fellow of IEEE.

Lecture    Abstract

Training deep neural   networks requires an exorbitant amount of computation resources, including a heterogeneous   mix of GPU and CPU devices. It is critical to place operations in a neural   network on these devices in an optimal way, so that the training process can   complete within the shortest amount of time. The state-of-the-art in the   literature uses a deep reinforcement learning method based on policy   gradients to solve this problem, but we believe that there remains ample room   for further improvements. In this talk, I will present our recent work   published in ICML 2018 and NeurIPS 2018 that uses proximal policy   optimization (PPO) and cross-entropy minimization to achieve significantly   better performance than the state-of-the-art. Our experiments with several   popular neural network training benchmarks have demonstrated clear evidence   of superior performance: with the same amount of learning time, our algorithm   leads to placements that have training times up to 60% shorter. This talk   will be targeting a general audience, and will therefore include a brief   tutorial on basic ideas in reinforcement learning algorithms.


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