10:45-12:15

Speaker:

Hardware/Software Co-Design of Deep Learning Accelerators

Prof. Yiyu Shi

University of Notre Dame, USA

Abstract：
The prevalence of deep neural networks today is supported by a variety of powerful hardware platforms including GPUs, FPGAs, and ASICs. A fundamental question lies in almost every implementation of deep neural networks: given a specific task, what is the optimal neural architecture and the tailor-made hardware in terms of accuracy and efficiency? Earlier approaches attempted to address this question through hardware-aware neural architecture search (NAS), where features of a fixed hardware design are taken into consideration when designing neural architectures. However, we believe that the best practice is through the simultaneous design of the neural architecture and the hardware to identify the best pairs that maximize both test accuracy and hardware efficiency. In this tutorial, we will introduce a series of novel co-exploration frameworks for neural architecture and various hardware platforms including FPGA, NoC, ASIC and Computing-in-Memory we developed in the past a few years, all of which are the first in the literature. We will demonstrate that the co-exploration concept greatly opens up the design freedom and pushes forward the Pareto frontier between hardware efficiency and test accuracy for better design tradeoffs.

Bio：

Prof. Yiyu Shi

Dr. Yiyu Shi is currently a professor in the Department of Computer Science and Engineering at the University of Notre Dame, the site director of National Science Foundation I/UCRC Alternative and Sustainable Intelligent Computing, and the director of the Sustainable Computing Lab (SCL). He is also a visiting scientist at Boston Children’s Hospital, the primary pediatric program of Harvard Medical School. He received his B.S. in Electronic Engineering from Tsinghua University, Beijing, China in 2005, the M.S and Ph.D. degree in Electrical Engineering from the University of California, Los Angeles in 2007 and 2009 respectively. His current research interests focus on hardware intelligence and biomedical applications. In recognition of his research, more than a dozen of his papers have been nominated for or awarded as the best paper in top conferences. He was also the recipient of 2021 IEEE Trans on CAD Donald O Pederson Best Paper Award, IBM Invention Achievement Award, Japan Society for the Promotion of Science (JSPS) Faculty Invitation Fellowship, Humboldt Research Fellowship, IEEE St. Louis Section Outstanding Educator Award, Academy of Science (St. Louis) Innovation Award, Missouri S&T Faculty Excellence Award, NSF CAREER Award, IEEE Region 5 Outstanding Individual Achievement Award, IEEE Computer Society TCVLSI Mid-Career Research Achievement Award, Facebook Research Award, among others. He has served on the technical program committee of many international conferences. He is the deputy editor-in-chief of IEEE VLSI CAS Newsletter, and an associate editor of various IEEE and ACM journals.