Meeting Location:

Storey Innovation Center 1400

Live Virtual Meeting Link:

https://teams.microsoft.com/l/meetup-join/19%3ameeting_OGQ4MTAwZmQtYTdhZS00NWRiLTg2N2QtNWU4MzI5NWNhOWMw%40thread.v2/0?context=%7b%22Tid%22%3a%224b2a4b19-d135-420e-8bb2-b1cd238998cc%22%2c%22Oid%22%3a%225fc2170a-7068-4a33-9021-df11b94ba696%22%7d

Speaker's Bio: Jae-sun Seo is an Associate Professor at the School of ECEE at Arizona State University. His research interests include efficient hardware design of machine learning / neuromorphic algorithms and integrated power management. He was a recipient of the IBM Outstanding Technical Achievement Award (2012), NSF CAREER Award (2017), and Intel Outstanding Researcher Award (2021).

Talk Abstract: Artificial intelligence (AI) and deep learning have been successful across many practical applications, but state-of-the-art algorithms require an enormous amount of computation, memory, and on-/off-chip communication. To bring expensive algorithms to a low-power processor, a number of digital CMOS ASIC solutions have been previously proposed, but limitations still exist on memory access and footprint. To improve upon the conventional row-by-row operation of memories, “in-memory computing (IMC)” designs have been proposed, which performs analog computation inside memory arrays by asserting multiple or all rows simultaneously. In this talk, we will present circuit-level, system-level, and algorithm-level techniques for designing SRAM IMC-based AI systems with high energy efficiency (compared to digital ASIC), high accuracy (similar to software baseline), and enhanced robustness (against adversarial attacks).