Dr Jianshi Tang of Tsinghua University

The rapid development of artificial intelligence, such as large language models, calls for more energy-efficient computing hardware, where fundamental breakthroughs from materials and devices to architectures are needed. To overcome the von Neumann bottleneck, neuromorphic computing with emerging devices, such as memristors, emerges as an attractive computing paradigm by mimicking human brain’s working mechanism for energy saving. This lecture intends to present a comprehensive review on the fundamental principles and applications of memristor-based neuromorphic computing. The selection of memristor materials and design of neuromorphic devices will be introduced. Then I will discuss the recent progress of large-scale integration of memristors with advanced Si CMOS. In particular, a variety of interesting demonstrations of energy-efficient computing-in-memory (CIM) on memristor crossbar arrays and prototype chips will be presented. I will then delve into two seminal applications of memristor-based CIM: the acceleration of artificial neural networks and the implementation of signal processing algorithms. Finally, I will conclude my talk with a future perspective in this field and highlight several noteworthy directions for future research.

Biography:

Dr Jianshi Tang is an Associate Professor and Vice Dean of the School of Integrated Circuits at Tsinghua University, where he received his bachelor’s degree in 2008. He received his PhD degree from University of California, Los Angeles in 2014. From 2015 to 2019, he worked at IBM Watson Research Center. He has received several awards including the First Prize in Natural Science of the Ministry of Education of China, Tsinghua Outstanding Young Faculty Award, MIT Technology Review “35 Innovators Under 35” China, IEEE Brain Best Paper Award, etc. His current research mainly focuses on emerging memory and neuromorphic computing. He has published over 200 papers, including Nature Electronics, Nature Nanotechnology, IEDM, VLSI, etc. He has also been granted more than 80 patents. Prof. Tang is an Editor of IEEE Transactions on Electron Devices, and Journal of Semiconductors. He is an IEEE senior member, and served as the Technical Program Committee Member for IEDM, IEEE-NANO, EDTM, CSTIC, etc.