Xinxin Wang

Xinxin Wang received her B.S. degree in Microelectronics from Peking University in 2018 and her Ph.D. in Electrical Engineering from the University of Michigan in 2023. During her doctoral studies, she worked on in-memory computing (IMC) architectures based on non-volatile memory technologies for deep neural network (DNN) and spiking neural network (SNN) inference, including tiled IMC macro design, hardware–software co-design, and AI simulator development.

She is currently a Postdoctoral Scholar co-advised by Professor Thierry Tambe and Professor H.-S. Philip Wong. Her current research focuses on AI accelerators with heterogeneous short-term and long-term memory, Gain Cell memory macro and compiler design, and high-density pixel circuit design leveraging BEOL Gain Cell memory.