The recent artificial intelligence (AI) boom has been primarily driven by three confluence forces: algorithms, big data, and computing power enabled by modern integrated circuits (ICs), including specialized AI accelerators. This talk will present a closed-loop perspective for synergistic AI and agile IC design with two main themes, AI for IC and IC for AI. As semiconductor technology enters the era of extreme scaling and heterogeneous integration, IC design and manufacturing complexities become extremely high. More intelligent and agile IC design technologies are needed than ever to optimize performance, power, manufacturability, design cost, etc., and deliver equivalent scaling to Moore’s Law. This talk will present our recent results leveraging modern AI and machine learning with domain-specific customizations for agile IC design and manufacturing, including DREAMPlace (DAC’19 and TCAD’21 Best Paper Awards) and its various extensions, DARPA-funded MAGICAL for analog/mixed-signal layout automation, LithoGAN for design-technology co-optimization, etc. Meanwhile, on the IC for AI frontier, customized ICs, including those with beyond-CMOS technologies, can drastically improve AI performance and energy efficiency by orders of magnitude. I will present a set of work on hardware and software co-design for optical neural networks and photonic ICs (2021 ACM Student Research Competition Grand Finals 1st Place, etc.). Closing the virtuous cycle between AI and IC holds great potential to advance the state-of-the-art of each other significantly.
David Z. Panis Silicon Laboratories Endowed Chair Professor at the Chandra Department of Electrical and Computer Engineering, The University of Texas at Austin. His research interests include electronic design automation, synergistic AI and IC co-optimizations, design for manufacturing, hardware security, and design/CAD for analog/mixed-signal and emerging technologies. He has published over 450 refereed journal/conference papers and 8 US patents. He has served in many editorial boards and conference committees, including various leadership roles such as DAC 2023 TPC Co-Chair, ICCAD 2019 General Chair, and ISPD 2008 General Chair. He has received many awards, including 20 Best Paper Awards (from TCAD, DAC, ICCAD, DATE, ASP-DAC, ISPD, HOST, SRC, IBM, etc.), SRC Technical Excellence Award, DAC Top 10 Author Award in Fifth Decade, ASP-DAC Frequently Cited Author Award, NSF CAREER Award, IBM Faculty Award (4 times), and many international CAD contest awards. He has held various advisory, consulting, or visiting positions in academia and industry, including MIT and Google. He has graduated 43 PhD students and postdocs who have won many awards, including ACM Student Research Competition Grand Finals 1st Place twice and Outstanding PhD Dissertation Awards from ACM/SIGDA and EDAA 5 times. He is a Fellow of ACM, IEEE, and SPIE.
The A3D3 Seminar is a monthly lecture series that hosts scholars working across applied areas of artificial intelligence, such as hardware algorithm co-development, high energy physics, multi-messenger astrophysics, and neuroscience. Our presenters come from all four domain fields and include occasional external speakers beyond the A3D3 science areas, governmental agencies and industry. The seminar will be recorded and published in YouTube. To receive future event update, subscribe here.