Education Class D1


Title: Introduction to Neuromorphic Computing

Instructor: Helen Li, Duke University

Abstract: The 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 the past seventy years already guided us to many remarkable successes in computing applications, e.g., artificial neural networks and machine learning. Inspired by the working mechanism of the human brain, neuromorphic system naturally possesses a massively parallel architecture with closely coupled memory, offering a great opportunity to break the “memory wall” in von Neumann architecture. The talk will start with a background introduction of neuromorphic computing, followed by examples of hardware acceleration schemes of learning and neural network algorithms and memristor-based computing engine. I will also share our prospects on the future technology challenges and advances of neuromorphic computing.

Bio: Hai (Helen) Li received her bachelor’s and master’s degrees from Tsinghua University, China, and her Ph.D. degree from Purdue University, USA. She is Clare Boothe Luce Professor and Associate Chair of the Electrical and Computer Engineering Department at Duke University. Before that, she was with Qualcomm Inc., San Diego, CA, USA, Intel Corporation, Santa Clara, CA, Seagate Technology, Bloomington, MN, USA, the Polytechnic Institute of New York University, Brooklyn, NY, USA, and the University of Pittsburgh, Pittsburgh, PA, USA. Her research interests include neuromorphic computing systems, machine learning and deep neural networks, memory design and architecture, and cross-layer optimization for low power and high performance. She has authored or co-authored more than 250 technical papers in peer-reviewed journals and conferences and a book entitled Nonvolatile Memory Design: Magnetic, Resistive, and Phase Changing (CRC Press, 2011). She received 9 best paper awards and an additional 9 best paper nominations from international conferences. Dr. Li serves/served as an Associate Editor of a number of IEEE/ACM journals. She was the General Chair or Technical Program Chair of multiple IEEE/ACM conferences. Dr. Li is a Distinguished Lecturer of the IEEE CAS society (2018-2019) and a distinguished speaker of ACM (2017-2020). Dr. Li is a recipient of the NSF Career Award, DARPA Young Faculty Award (YFA), TUM-IAS Hans Fischer Fellowship from Germany, and ELATE Fellowship (2020). Dr. Li is an IEEE fellow and a distinguished member of the ACM.