ACM-W Distinguished Speaker
Brain Inspired Computing: The Extraordinary Voyages in Known and Unknown Worlds
|Speaker:||Dr. Hai Li
||Monday, November 6, 2017
||4:00pm - 5:00pm
||D344 LSRC, Duke
||Coffee, bagels and donuts will be provided.
Human brain is the most sophisticated organ that nature ever builds. Building a machine that can function like a human brain, indubitably, is the ultimate dream of a computer architect. Although
we have not yet fully understood the working mechanism of human brains, the part that we have learned in past seventy years already guided us to many remarkable successes in computing applications, e.g., artificial neural network and machine learning. The recently emerged research on “neuromorphic computing”, which stands for hardware acceleration of brain-inspired computing, has become one of the most active areas in computer engineering. The talk will start
with a background introduction of neuromorphic computing, followed by two examples of hardware acceleration schemes of learning and neural network algorithms and memristor-based computing engine, respectively. At the end, I will share our prospects on the future technology challenges and advances of neuromorphic computing.
Dr. Hai "Helen" Li received her B.S. and M.S. degrees from Tsinghua University, Beijing, China and Ph.D. degree in 2004 from Purdue University, West Lafayette, IN, USA. She is currently the Clare Boothe Luce Associate Professor of Electrical and Computer Engineering Department at Duke University. Dr. Li also serves as the director of the Duke Center of Evolutionary Intelligence. Prior to joining Duke University, she worked for Qualcomm Inc., Intel Corp., Seagate Technology, Polytechnic Institute of New York University, and the University of Pittsburgh. Dr. Li has authored or co-authored over 200 technical papers published in peer-reviewed journals and a book entitled Nonvolatile Memory Design: Magnetic, Resistive, and Phase Changing (CRC Press, 2011). Her research interests include emerging memory design and architecture, brain-inspired computing systems, and hardware-software co-design of deep neural network acceleration.
Dr. Li serves as an Associate Editor of IEEE Transactions on Computer Aided Design (TCAD), IEEE Transactions on Multi-Scale Computing Systems (TMSCS), IEEE Transactions on Very Large Scale Integration (TVLSI) Systems, the IEEE Consumer Electronics Magazine (CEM), ACM Transactions on Design Automation of Electronic Systems (TODAES), and IET Cyber-Physical Systems: Theory & Applications (IET-CPS). She was the Guest Editor for IEEE TCAD, IEEE TNANO, IEEE JETCAS, ACM JETC, IET CPS, and VLSI Integration. She was the General Chair of ISVLSI, ICCE, ISQED and GLSVLSI, and the Technical Program Chair
of SoCC, iNIS, GLSVLSI. She also served on the ACM/SIGDA Outstanding PhD Dissertation Award Selection Committee, the Program chair for ACM SIGDA summer school (DASS), the Executive Committee of ISVLSI, GLSVLSI and iNIS, and the Technical Program Committee members of over 20 international conference series.
ACM International Conference on Great Lakes Symposium on VLSI (GLSVLSI) in 2013, the Best Paper Award from ACM/IEEE International Symposium on Low Power Electronics and Design (ISLPED) in 2010, and the Best Paper Award from International Symposium on Quality Electronic Design (ISQED) in 2008. In addition to these awards, she also received seven Best Paper Nominations from Design Automation Conference (DAC), International Conference on Computer-Aided Design (ICCAD), International Symposium on Low Power Electronics and Design (ISLPED), Asia and South Pacific Design Automation Conference (ASPDAC), Design, Automation & Test in Europe Conference and Exhibition (DATE), and International Symposium on Quality Electronic Design (ISQED). She is a senior member of IEEE and ACM.