报告题目：Efficient Digital Design and Implementation with Machine Learning in EDA

演讲摘要：EDA technology has achieved remarkable progress over the past decades. However, chip design is not completely automatic yet in general. For example, automation of EDA flow is still largely restricted to individual tools with little interplay across tools and design steps, and tools in early steps cannot efficiently judge if their solutions eventually lead to satisfactory designs. In addition, solutions are constructed from scratch even if similar optimizations have already been performed repeatedly. We believe such limitations can be largely addressed by knowledge reuse with machine learning, whose major strength is to explore highly complex correlations between design stages based on prior data. In this talk, I will share our recent research about customized ML algorithms in EDA. They cover a wide range of design stages from the RTL level to post-routing, solving primary chip-design problems including power, timing, interconnect, IR drop, routability, and design flow tuning. After introducing these research efforts systematically, I will present two latest progresses with more details. They are power estimation and monitoring implemented at the RTL level, and efficient routability prediction performed during layout. Finally, I will share our experience and vision in enabling efficient digital design and implementation with machine learning in EDA.

讲者简介：Yiran Chen received B.S (1998) and M.S. (2001) from Tsinghua University and Ph.D. (2005) from Purdue University. After five years in industry, he joined University of Pittsburgh in 2010 as Assistant Professor and then was promoted to Associate Professor with tenure in 2014, holding Bicentennial Alumni Faculty Fellow. He is now the Professor of the Department of Electrical and Computer Engineering at Duke University and serving as the director of the NSF AI Institute for Edge Computing Leveraging the Next-generation Networks (Athena) and the NSF Industry–University Cooperative Research Center (IUCRC) for Alternative Sustainable and Intelligent Computing (ASIC), and the co-director of Duke Center for Computational Evolutionary Intelligence (CEI). His group focuses on the research of new memory and storage systems, machine learning and neuromorphic computing, and mobile computing systems. Dr. Chen has published 1 book and about 500 technical publications and has been granted 96 US patents. He has served as the associate editor of a dozen international academic transactions/journals and served on the technical and organization committees of more than 60 international conferences. He is now serving as the Editor-in-Chief of the IEEE Circuits and Systems Magazine. He received seven best paper awards, one best poster award, and fourteen best paper nominations from international conferences and workshops. He received many professional awards and is the distinguished lecturer of IEEE CEDA (2018-2021). He is a Fellow of the ACM and IEEE and now serves as the chair of ACM SIGDA.