專用集成電路與系統國家重點實驗室

講座信息

時  間：3月13日下午2:00-3:00
地  點：張江校區微電子樓389
報告人：陳怡然    教授（美國杜克大學）

Title: How to Obtain and Run Light and Efficient Deep Learning Networks

Abstract: 
Fast growth of the computation cost associated with training and testing of deep neural networks (DNNs) inspired various acceleration techniques. Reducing topological complexity and simplifying data representation of neural networks are two approaches that popularly adopted in deep learning society: many connections in DNNs can be pruned and the precision of synaptic weights can be reduced, respectively, incurring no or minimum impact on inference accuracy. However, the practical impacts of hardware design are often ignored in these algorithm-level techniques, such as the increase of the random accesses to memory hierarchy and the constraints of memory capacity. On the other side, the limited understanding about the computational needs at algorithm level may lead to unrealistic assumptions during the hardware designs. In this talk, we will discuss this mismatch and show how we can solve it through an interactive design practice across both software and hardware levels.

Bio: 
Yiran Chen received B.S and M.S. from Tsinghua University and Ph.D. from Purdue University in 2005. After five years in industry, he joined University of Pittsburgh in 2010 as Assistant Professor and then promoted to Associate Professor with tenure in 2014, held Bicentennial Alumni Faculty Fellow. He now is a tenured Associate Professor of the Department of Electrical and Computer Engineering at Duke University and serving as the co-director of Duke Center for Evolutionary Intelligence (CEI), focusing on the research of new memory and storage systems, machine learning and neuromorphic computing, and mobile computing systems. Dr. Chen has published one book and more than 300 technical publications and has been granted 93 US patents. He is the associate editor of IEEE TNNLS, IEEE D&T, IEEE ESL, ACM JETC, and ACM TCPS, and served on the technical and organization committees of more than 40 international conferences. He received 6 best paper awards and 12 best paper nominations from international conferences. He is the recipient of NSF CAREER award and ACM SIGDA outstanding new faculty award. He is the Fellow of IEEE.

聯系人🏩：曾璇、陶俊