Presentation - Machine Learning in EDA: When and How
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Abstract
Machine learning is a powerful technique that can derive knowledge from large data set, and provide prediction and modeling. Since VLSI chip designs have extremely high complexity and gigantic data, recently there has been a surge in applying and adapting machine learning to accelerate the design closure. In this talk, I will discuss when and how to apply machine learning in Electronic Design Automation (EDA) improving the efficiency and quality of the design process. Furthermore, I highlight distinct challenges in EDA, including improved netlist representation, advanced timing modeling, netlist-layout multimodality, and constrained AIGC.
Biography - Dr. Bei Yu
Prof. Bei Yu is an Associate Professor at the Department of Computer Science and Engineering, The Chinese University of Hong Kong. He received the Ph.D degree from Electrical and Computer Engineering, University of Texas at Austin, USA in 2014, and an M.S. degree in Computer Science from Tsinghua University, China in 2010. His current research interests include machine learning and combinatorial algorithm with applications in electronic design automation (EDA) and computer vision. He has served as TPC Chair of 1st ACM/IEEE Workshop on Machine Learning for CAD (MLCAD), served in the program committees of DAC, ICCAD, DATE, ASPDAC, ISPD, the editorial boards of ACM Transactions on Design Automation of Electronic Systems (TODAES), Integration, the VLSI Journal, and IET Cyber-Physical Systems: Theory & Applications. He was Editor of IEEE TCCPS Newsletter and Past Chair of IEEE CEDA Hong Kong Chapter. Prof. Yu received 11 Best Paper Awards from International Conference on Computer-Aided Design (ICCAD) 2024 & 2021 & 2013, IEEE Transactions on Semiconductor Manufacturing 2022, Design, Automation and Test in Europe Conference (DATE) 2022, Asia and South Pacific Design Automation Conference (ASPDAC) 2021 & 2012, International Conference on Tools with Artificial Intelligence (ICTAI) 2019, Integration, the VLSI Journal in 2018, International Symposium on Physical Design (ISPD) 2017, SPIE Advanced Lithography Conference 2016, eight other Best Paper Award Nominations (ISPD 2024, ASPDAC 2023, MLCAD 2022, DATE 2021, ASPDAC 2019, DAC 2014, ASPDAC 2013, and ICCAD 2011), seven ICCAD/ISPD/ICDAR contest awards, IBM Ph.D. Scholarship in 2012, SPIE Education Scholarship in 2013, EDAA Outstanding Dissertation Award in 2014, DAC Under-40 Innovator Award, ACM SIGDA Meritorious Service Award, IEEE CEDA Ernest S. Kuh Early Career Award, and Hong Kong RGC Research Fellowship Scheme (RFS) Award in 2024.
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