Department of Electronic and Computer Engineering Seminar - Energy-Efficient Hybrid In-Memory Computing with CMOS and Emerging Nanotechnologies for Edge Artificial Intelligence

10:30am - 11:30am
Rm 2515, ECE Conference Room, 2/F (near Lift 25, 26), Academic Building

Deep neural networks are getting complex and fuel the explosion of parameters. Efficiently implementing the large-scale NNs at the resource-constrained edge devices is pivotal for widespread AI applications. Computing-in-Memory (CIM) is an efficient solution to accelerate NNs by merging the dominant matrix-vector-multiplication operations into memories. Among various memory types, the non-volatile resistive RAM (ReRAM) emerges as high-density on-chip storage with limited endurance, while the SRAM possess high data-access performance at cost of large cell area. As a new paradigm of CIM beyond the single memory type, hybrid memories in-computing brings new opportunities to boost the energy efficiency of large NN acceleration.     

In this talk, several hybrid CIM design techniques with CMOS and emerging nanotechnologies will be presented. First, we will review the latest development in hybrid CIM. Then, we will present a new non-volatile SRAM-CIM (nvSRAM-CIM) macro and architecture design by combining non-volatile high-density ReRAM devices with robust and energy-efficient SRAM cells for large NN inference. After that, we will introduce another new design on hybrid three-dimension RRAM- and SRAM-CIM architecture for multi-task transformer acceleration based on a CIM-friendly transfer learning algorithm. Finally, perspectives of circuit designs for future edge AI will be discussed. 

Event Format
Speakers / Performers:
Dr. Yanan Sun
Department of Micro-Nano Electronics, Shanghai Jiao Tong University

Dr. Yanan Sun is an Associate Professor with the Department of Micro-Nano Electronics, Shanghai Jiao Tong University. She received the BE degree in microelectronics from Shanghai Jiao Tong University in 2009, and then completed the PhD degree in Electronic and Computer Engineering from The Hong Kong University of Science and Technology in 2015. Her research area is energy-efficient VLSI circuit and system design with CMOS and beyond-CMOS technologies.

Dr. Sun has published 70+ papers on IEEE transactions including TCAS-I/II, TVLSI, TCAD, TED, JSSC, and IEEE/ACM conferences including DAC, DATE, ICCAD, ISSCC. Dr. Sun received the best paper award nomination in IEEE DATE 2020 and IEEE ICTA 2023, and the best paper award (first place) in IEEE ICM 2014. She was awarded Shanghai Youth Science and Technology Talents Sailing Award in 2017. Dr. Sun is serving as an associate editor of Microelectronics Journal since 2015. She is also serving as TPC members on IEEE/ACM GLSVLSI, ISQED, ICTA, and APCCAS. Dr. Sun is senior member of IEEE.

Recommended For
Faculty and staff
PG students
Department of Electronic & Computer Engineering
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