报告题目：Accelerating Deep Convolutional Networks
Abstract：Deep neural networks (DNNs) have achieved significant success in a variety of real world applications. However, tons of parameters in the networks restrict the efficiency of neural networks due to the large model size and the intensive computation. To address this issue, various compression and acceleration techniques have been investigated. In this talk I will introduce state-of-the-art techniques in DNN accelerating techniques from the following three perspectives: 1) how we can accelerate accurate DNN inference; 2) how we can accelerate inaccurate DNN inference; 3) how we can accelerate DNN design space exploration. In addition, I will also discuss some computer science & engineering skills that can contribute to a successful research career.
Speaker Bio： Prof. Bei Yu received his Ph.D. degree from the Department of Electrical and Computer Engineering, University of Texas at Austin in 2014. He is currently in the Department of Computer Science and Engineering, The Chinese University of Hong Kong. He has served in the editorial boards of Integration, the VLSI Journal and IET Cyber-Physical Systems: Theory & Applications. He has received four Best Paper Awards at ISPD 2017, SPIE Advanced Lithography Conference 2016, ICCAD 2013, and ASPDAC 2012, three other Best Paper Award Nominations at DAC 2014, ASPDAC 2013, ICCAD 2011, and four ICCAD/ISPD contest awards.