In-Sensor AI Computing for Efficient Real-Time Machine Perception

Artificial intelligence (AI) and machine learning (ML) technologies have fueled many burgeoning applications from on-device learning 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 information processing ability innate in the analog/mixed-signal (AMS) domain by embedding AI computation directly inside the pixel circuits, delivering much-improved performance and efficiency. I will first introduce our design of in-sensor learned compressive acquisition (LeCA) and then talk about a novel in-sensor architecture that supports real-time gaze tracking, a key primitive in AR/VR applications. Finally, I will present CamJ, a first-of-its-kind energy modeling framework that empowers designers to navigate the large algorithm-hardware co-design space of computational image sensors.
Biography:
Dr. Xuan Zhang is an Associate Professor in the Electrical and Computer Engineering Department at Northeastern University. She works across the fields of integrated circuits/VLSI design, computer architecture, and electronic design automation. Dr. Zhang is an IEEE Women in Engineering (WiE) Distinguished Lecturer for 2023-2024, IEEE Circuits and Systems Society (CAS) Distinguished Lecturer for 2022-2023, and the recipient of NSF CAREER Award in 2020. She currently serves as the Associate Editor-in-Chief at IEEE Transactions on Circuits and Systems I (TCAS-I) and Associate Editor at IEEE Transactions on Computer-Aided Designs (TCAD). Her work has received numerous best paper awards and nominations including ISLPED Best Paper Award in 2022, AsianHOST Best Paper Award in 2020, DATE Best Paper Award in 2019, and nomination for Best Paper Awards at DAC 2022, ASP-DAC 2021, MLCAD 2020, DATE 2019, and DAC 2017.
Co-sponsored by: IEEE
Virtual: https://events.vtools.ieee.org/m/436745