Keynote Speakers 主讲专家
Prof. David Z. Pan, IEEE Fellow and SPIE Fellow
Silicon Labs Endowed Chair Professor
The University of Texas at Austin, United States
David Z. Pan is currently Silicon Labs Endowed Chair Professor at the Department of Electrical and Computer Engineering, The University of Texas at Austin. His research interests include bidirectional AI and IC interactions, electronic design automation, design for manufacturing, hardware security, and CAD for analog/mixed-signal ICs and emerging technologies. He has published over 380 refereed journal/conference papers and 8 US patents. He has served in many journal editorial boards and conference committees, including various leadership roles such as ICCAD 2019 General Chair, ASP-DAC 2017 TPC Chair, and ISPD 2008 General Chair. He has received many awards, including SRC Technical Excellence Award, 19 Best Paper Awards (ISPD 2020, ASP-DAC 2020, DAC 2019, GLSVLSI 2018, VLSI Integration 2018, HOST 2017, SPIE 2016, ISPD 2014, ICCAD 2013, ASP-DAC 2012, ISPD 2011, IBM Research 2010 Pat Goldberg Memorial Best Paper Award in CS/EE/Math, ASP-DAC 2010, DATE 2009, ICICDT 2009, SRC Techcon in 1998, 2007, 2012 and 2015), DAC Top 10 Author Award in Fifth Decade, ASP-DAC Frequently Cited Author Award, Communications of ACM Research Highlights, ACM/SIGDA Outstanding New Faculty Award, NSF CAREER Award, IBM Faculty Award (4 times), and many international CAD contest awards. He has graduated 36 PhD students and postdocs who have won many awards, including the First Place of ACM Student Research Competition Grand Finals in 2018, ACM/SIGDA Student Research Competition Gold Medal (twice), ACM Outstanding PhD Dissertation in EDA Award (twice), EDAA Outstanding Dissertation Award (twice), etc. He is a Fellow of IEEE and SPIE.
AI for IC and IC for AI: Closed-Loop
Perspectives and Recent Results
Abstract: The recent artificial intelligence (AI) boom has been largely driven by three confluence forces: algorithms, 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 the semiconductor technology enters the era of extreme scaling, 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 to deliver equivalent scaling to Moore’s Law. I will present some recent results leveraging modern AI and machine learning advancement with domain-specific customizations for agile IC design and manufacturing closure. Meanwhile, customized ICs, including those with beyond-CMOS technologies can drastically improve AI performance and energy efficiency by orders of magnitude. I will present some recent results on hardware/software co-design for high performance and energy-efficient optical neural networks. The bidirectional feedback of synergistic AI and IC design holds great potential to significantly advance the state-of-the-art of each other.
Prof. Ljiljana Trajkovic, IEEE Fellow
Simon Fraser University, Canada
Ljiljana Trajkovic received the Dipl. Ing. degree from
University of Pristina, Yugoslavia, in 1974, the M.Sc. degrees in electrical
engineering and computer engineering from Syracuse University, Syracuse, NY, in
1979 and 1981, respectively, and the Ph.D. degree in electrical engineering from
University of California at Los Angeles, in 1986.
She is currently a Professor in the School of Engineering Science at Simon Fraser University, Burnaby, British Columbia, Canada. From 1995 to 1997, she was a National Science Foundation (NSF) Visiting Professor in the Electrical Engineering and Computer Sciences Department, University of California, Berkeley. She was a Research Scientist at Bell Communications Research, Morristown, NJ, from 1990 to 1997, and a Member of the Technical Staff at AT&T Bell Laboratories, Murray Hill, NJ, from 1988 to 1990. Her research interests include high-performance communication networks, control of communication systems, computer-aided circuit analysis and design, and theory of nonlinear circuits and dynamical systems.
Dr. Trajkovic serves as IEEE Division X Delegate/Director (2019–2020) and served as IEEE Division X Delegate-Elect/Director-Elect (2018). She served as Senior Past President (2018–2019), Junior Past President (2016–2017), President (2014–2015), President-Elect (2013), Vice President Publications (2012–2013, 2010–2011), Vice President Long-Range Planning and Finance (2008–2009), and a Member at Large of the Board of Governors (2004–2006) of the IEEE Systems, Man, and Cybernetics Society. She served as 2007 President of the IEEE Circuits and Systems Society and a member of its Board of Governors (2004–2005, 2001–2003). She is Chair of the IEEE Circuits and Systems Society joint Chapter of the Vancouver/Victoria Sections. She was Chair of the IEEE Technical Committee on Nonlinear Circuits and Systems (1998). She is General Co-Chair of SMC 2020 and SMC 2020 Workshop on BMI Systems and served as General Co-Chair of SMC 2019 and SMC 2018 Workshops on BMI Systems, SMC 2016, and HPSR 2014, Special Sessions Co-Chair of SMC 2017, Technical Program Chair of SMC 2017 and SMC 2016 Workshops on BMI Systems, Technical Program Co-Chair of ISCAS 2005, and Technical Program Chair and Vice General Co-Chair of ISCAS 2004. She served as an Associate Editor of the IEEE Transactions on Circuits and Systems (Part I) (2004–2005, 1993–1995), the IEEE Transactions on Circuits and Systems (Part II) (2018, 2002–2003, 1999–2001), and the IEEE Circuits and Systems Magazine (2001–2003). She is a Distinguished Lecturer of the IEEE Systems, Man, and Cybernetics Society (2020–2021) and the IEEE Circuits and Systems Society (2010–2011, 2002–2003). She is a Professional Member of IEEE-HKN and a Life Fellow of the IEEE.
Detecting Network Anomalies and Intrusions
Abstract: The Internet, social networks, power grids, gene regulatory networks, neuronal systems, food webs, social systems, and networks emanating from augmented and virtual reality platforms are all examples of complex networks. Collection and analysis of data from these networks is essential for their understanding. Traffic traces collected from various deployed communication networks and the Internet have been used to characterize and model network traffic, analyze network topologies, and classify network anomalies. Data mining and statistical analysis of network data have been employed to determine traffic loads, analyze patterns of users' behavior, and predict future network traffic while spectral graph theory has been applied to analyze network topologies and capture historical trends in their development. Machine learning techniques have proved valuable for predicting anomalous traffic behavior and for classifying anomalies and intrusions in communication networks. Applications of these tools help understand the underlying mechanisms that affect behavior, performance, and security of computer networks.