Date(s) - 10/06/2021
12:00 - 13:00
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Dr. David Pan, The University of Texas at Austin
This talk will present some recent trends and efforts toward agile and open electronic design automation (EDA), in particular leveraging AI/machine learning with domain-specific customizations. I will first show how we leverage deep learning hardware and software to develop an open-source VLSI placement engine, DREAMPlace [DAC’19 Best Paper Award, TCAD 2020], which is around 40x faster than the previous state-of-the-art academic global placer with high quality. DREAMPlace 2.0 and 3.0 have also been released to tackle detailed placement acceleration and region constraints. I will then present the DARPA-funded project MAGICAL which leverages both machine and human intelligence to produce fully automated analog layouts from netlists to GDSII. MAGICAL 1.0 has been open-sourced, and validated with a silicon-proven 40nm 1GS/s ∆Σ ADC [CICC’21]. I will also discuss the challenges and opportunities.
Dr. David Z. Pan is a Professor in the Department of Electrical & Computer Engineering at The University of Texas at Austin and holds the Silicon Laboratories Endowed Chair in Electrical Engineering.
He received his B.S. degree from Peking University, and his M.S./Ph.D. degrees from University of California at Los Angeles (UCLA). From 2000 to 2003, he was a Research Staff Member with the IBM T. J. Watson Research Center, Yorktown Heights, NY. His research is mainly focused on electronic design automation, synergistic AI/IC co-optimizations, domain-specific accelerators, design for manufacturing, hardware security, and design/CAD for analog/mixed-signal and emerging technologies. He has published over 420 technical papers in refereed journals and conferences, and is the holder of 8 U.S. patents. He has held various advisory, consulting, or visiting positions in academia and industry, such as MIT and Google. He has graduated over 40 PhD/postdoc students at UT Austin who are now holding key academic and industry positions.
He has served as a Senior Associate Editor of ACM Transactions on Design Automation of Electronic Systems (TODAES), an Associate Editor of IEEE Design & Test, IEEE Transactions on CAD, IEEE Transactions on VLSI, IEEE Transactions on CAS-I, IEEE Transactions on CAS-II, IEEE CAS Society Newsletter, Science China Information Sciences, and Journal of Computer Science and Technology. He has served in the Executive and Program Committees of many major conferences, including DAC, ICCAD, ASPDAC, and ISPD. He has served as the General Chair of ICCAD 2019 and ISPD 2008, Program Chair of ICCAD 2018 and ASPDAC 2017, and DAC 2014 Tutorial Chair and DAC 2022 Panel Chair. He served in the ACM/SIGDA Executive Committee as the Award Chair from 2018 to 2021.
He has received the 2013 SRC Technical Excellence Award, DAC Top 10 Author in Fifth Decade, DAC Prolific Author Award, ASP-DAC Frequently Cited Author Award, 20 Best Paper Awards (TCAD 2021, ISPD 2020, ASP-DAC 2020, DAC 2019, GLSVLSI 2018, VLSI Integration 2018, HOST 2017, SPIE-AL 2016, ISPD 2014, ICCAD 2013, ASPDAC 2012, ISPD 2011, IBM Research Pat Goldberg Memorial Best Paper Award 2010 in CS/EE/Math, ASPDAC 2010, DATE 2009, ICICDT 2009, SRC Techcon 2015, 2012, 2007 and 1998), Communications of the ACM Research Highlights (2014), ACM/SIGDA Outstanding New Faculty Award (2005), NSF CAREER Award (2007), UCLA Engineering Distinguished Young Alumnus Award (2009), UT Austin RAISE Faculty Excellence Award (2014), IBM Faculty Award four times, SRC Inventor Recognition Award three times, Cadence Academic Collaboration Award (2019), and a number of international CAD contest awards, among others. His students have won many awards, including the First Place of ACM Student Research Competition Grand Finals twice in 2018 and 2021, ACM/SIGDA Student Research Competition Gold Medal (three times), ACM Outstanding PhD Dissertation in EDA (twice), EDAA Outstanding Dissertation Award (twice), and so on. He is a Fellow of ACM, IEEE and SPIE.
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