Neural Acceleration with Full Stack Optimization

Speaker:
Professor Meng Li
Assistant Professor
Institute for Artificial Intelligence, School of Integrated Circuits, Peking University

Abstract:
Recent years have witnessed the fast evolution of AI and deep learning (DL) in the field computer vision, natural language processing, etc. Though promising, DL faces serious challenges due to the exponential network scaling and network heterogeneity. In this talk, I will discuss some of our recent works that leverage network/hardware co-design and co-optimization to improve the efficiency for DL. I will cover our recent works on tiny language model for MCUs, memory-aware scheduling, and hardware accelerator designs based on a new computing paradigm, i.e., stochastic computing. I will also discuss interesting future directions to further improve the efficiency and security for efficient AI.

Biography:
Prof. Meng Li is currently a tenure-track assistant professor in Peking University, jointly affiliated with Institute for Artificial Intelligence and School of Integrated Circuits. Before joining Peking University, he was staff research scientist and tech lead in Meta Reality Lab, the world’s largest social media company, focusing on research and productization of efficient AI algorithms and hardware/systems for next generation AR/VR devices. Dr. Li received his Ph.D. degree from the University of Texas at Austin in 2018 and his bachelor degree from Peking University in 2013.

Prof. Meng Li’s research interests lie in the field of efficient and secure multi-modal AI acceleration algorithms and hardware. He has published more than 60 papers and received two best paper awards from HOST 2017 and GLSVLSI 2018. He also receives EDAA Outstanding Dissertation Award, First Place in ACM Student Research Competition Grand Final (Graduate Category), Best Poster Awards in ASPDAC Student Research Forum, etc.

Enquiries: Jeff Liu ( jeffliu@cse.cuhk.edu.hk )