xuan zhang

Speaker: Xuan Zhang

Date: Tuesday, April 18th, 2023

Time: 3:30 - 4:30pm

Location: HFH 1132

Host: Lei Li

Title: Brain-Inspired AI Computing in the Analog Domain

Abstract: 

Artificial intelligence (AI) and machine learning (ML) technologies have fueled many burgeoning applications from on-device learning and personalized recommendations to self-driving cars and collaborative robots. Despite these unprecedented advancements, the holy grail of enabling fully autonomous machine intelligence remains far from our grasp. One key challenge is the lack of performant and efficient hardware implementation, especially in the case of embedded/edge devices with rich sensory inputs yet stringent resource constraints.

In this talk, I will present our work to tackle this challenge from a unique angle that leverages brain/neuro-inspired techniques beyond the conventional binary digital abstractions.  Specifically, we leverage information processing ability innate in the analog/mixed-signal (AMS) domain and exploit co-located compute and memory organization. I will introduce how our method re-imagines the design of the analog-to-digital interfaces, as well as transforms the readout peripheral circuits in Processing-in-Memory (PIM) and In-Sensor visual computing systems, delivering much-improved performance and efficiency. I will conclude the talk with a vision for future sensor-rich intelligent devices powered by distinctive yet complementary computing paradigms as an enabling technology for experiential and embodied AI and internet of connected edge intelligence.

Bio: 

Dr. Xuan ‘Silvia’ Zhang is an Associate Professor in the Preston M. Green Department of Electrical and Systems Engineering at Washington University in St. Louis. She received her B. Eng. degree in Electronic Engineering from Tsinghua University in China, and her MS and Ph.D. degrees in Electrical and Computer Engineering from Cornell University. She works across the fields of integrated circuits/VLSI design, computer architecture, and electronic design automation. Her research interests include novel circuits and architecture for efficient machine learning and artificial intelligence, adaptive resource management for autonomous systems, and system security in analog and physical domains. Dr. Zhang is an IEEE Circuits and Systems Society (CAS) Distinguished Lecturer for 2022-2023, the winner of IEEE St. Louis Section Outstanding Researcher and Outstanding Women in Engineering in 2022, and the recipient of NSF CAREER Award in 2020. She received ISLPED Best Paper Award in 2022, AsianHOST Best Paper Award in 2020, DATE Best Paper Award in 2019, and ISLPED Design Contest Award in 2013. Her work has also been nominated for Best Paper Awards at DAC 2022, ASP-DAC 2021, MLCAD 2020, DATE 2019, and DAC 2017.