February 2023

Wang Ying

Associate Professor

Institute of Computing Technology, Chinese Academy of Sciences.

Email:

wangying2009@ict.ac.cn

Personal webpage

https://wangying-ict.github.io/

Research interests

Domain-Specific chips, processor architecture and design automation

Short bio

Ying Wang is an associate professor in Institute of Computing Technology, Chinese Academy of Sciences. Wang’s research expertise is focused on VLSI testing, reliability and the design automation of domain-specific processors such as accelerators for computer vision, deep learning, graph computing and robotics. His group has conducted pioneering work in the open-source frameworks for automated neural network accelerator generation and customization. He has published more than 30 papers at DAC, and over 120 papers on other IEEE/ACM conferences and journals. He holds over 30 patents related to chip design. Wang is also a co-founder of Jeejio Tech in Beijing, which is granted the Special Start-up Award of the year 2018 by Chinese Academy of Sciences. Among Wang’s honors, it also includes the Young Talent Development Program Awardee from Chinese Association of Science and Technology (as one of the two awardees of computer science in 2016), 2017 CCF Intel outstanding researcher award, 2019 Early Career Award from Chinese Computer Federation and etc. He is the recipient of Under 40 Innovator award of DAC at 2021. Dr. Wang has also received several awards from international conferences, including the winner of System Design Contest at DAC 2018 and IEEE rebooting LPIRC 2016, the Best Paper Award at ITC-Asia 2018, GLSVLSI2021 (2nd place), ICCD 2019, and the best paper of 2011 IEEE Transaction on Computers, as well as the best paper nominee in ASPDAC.

Research highlights

Dr. Wang’s innovative research in the DeepBurning project has significantly contributed to one of the viable approaches toward automatic specialized accelerator generation and is considered one of the representative works in this area, which is to start from the software framework to directly generate a specialized circuit design implemented on FPGA or ASICs. After the initial project of DeepBurning1.0, he continues to pioneer several on-going works including ELNA (DAC2017), Dadu (DAC2018), 3D-Cube (DAC2019), DeepBurning-GL (ICCAD2020) and DeepBurning-Seg (Micro-2022), which also follows the same technical route of automatic hardware accelerator generation but has been extended to different applications and architectures. Also, the DeepBurning series not only develops horizontally to different areas, but also vertically go to the high level processor design stacks including early-stage design parameter exploration, ISA extension and compiler-hardware co-design. In general, his holistic work on this field has attracted considerable attention from different Chinese EDA companies. Based on the agile chip customization technology initiated by Dr. Wang, his company, Jeejio, is able to develop highly-customized chip solutions at a relatively low cost, and help its customers stay competitive in the niche IoT markets. Dr. Wang’s team has proposed the RISC-V compatible Sage architecture that can be used to customize AIoT SoC solution with user-redeemable computing power, for audio/video/image processing capability and also automotive scenarios.