Ph.D. in Computer Science & Engineering |
Don’t be encumbered by history, just go out and do something wonderful.- Robert Noyce
I received the B.E. degree in software engineering from the University of Electronic Science and Technology of China, Chengdu, China, in 2020 and the Ph.D. degree in computer science and engineering from The Chinese University of Hong Kong, Hong Kong, in 2024, under the supervision of Prof. Bei Yu and co-supervised by Prof. Martin D.F. Wong. My research interests include computer architecture and electronic design automation. I received the William J. McCalla Best Paper Award from ICCAD 2021 and the Best Paper Award Nomination from ISPD 2024.
Computer Architecture & Computer Systems
Electronic Design Automation (EDA)
Ph.D. Computer Science & Engineering, The Chinese University of Hong Kong, Aug. 2020 - Jul. 2024
B.Eng. Software Engineering, The University of Electronic Science and Technology of China, Sep. 2016 - Jul. 2020
[C18] Peng Xu, Su Zheng, Yuyang Ye, Chen Bai, Siyuan Xu, Hao Geng, Tsung-Yi Ho, Bei Yu, “RankTuner: When Design Tool Parameter Tuning Meets Preference Bayesian Optimization”, IEEE/ACM International Conference on Computer-Aided Design (ICCAD), New Jersey, Oct. 27–31, 2024.
[C17] Yuanhang Gao, Donger Luo, Chen Bai, Bei Yu, Hao Geng, Qi Sun, Cheng Zhuo, “Is Vanilla Bayesian Optimization Enough for High-Dimensional Architecture Design Optimization?”, IEEE/ACM International Conference on Computer-Aided Design (ICCAD), New Jersey, Oct. 27–31, 2024.
[C16] Lancheng Zou, Wenqian Zhao, Shuo Yin, Chen Bai, Qi Sun, Bei Yu, “BiE: Bi-Exponent Block Floating-Point for Large Language Models Quantization”, International Conference on Machine Learning (ICML), Vienna, Jul. 21–27, 2024. (paper) (slides) (poster)
[C15] Donger Luo, Qi Sun, Xinheng Li, Chen Bai, Bei Yu, Hao Geng, “Knowing The Spec to Explore The Design via Transformed Bayesian Optimization”, ACM/IEEE Design Automation Conference (DAC), San Francisco, Jun. 23–27, 2024. (paper) (slides)
[C14] Tong Qiao, Jianlei Yang, Yingjie Qi, Ao Zhou, Chen Bai, Bei Yu, Weisheng Zhao, Chunming Hu, “GNNavigator: Towards Adaptive Training of Graph Neural Networks via Automatic Guideline Exploration”, ACM/IEEE Design Automation Conference (DAC), San Francisco, Jun. 23–27, 2024. (paper)
[C13] Chen Bai, Jianwang Zhai, Yuzhe Ma, Bei Yu, Martin D.F. Wong, “Towards Automated RISC-V Microarchitecture Design with Reinforcement Learning”, AAAI Conference on Artificial Intelligence (AAAI), Vancouver, Feb. 20–27, 2024. (paper) (slides) (code) (poster) (video)
[C12] Yuan Pu, Tinghuan Chen, Zhuolun He, Chen Bai, Haisheng Zheng, Yibo Lin, Bei Yu, “IncreMacro: Incremental Macro Placement Refinement”, ACM International Symposium on Physical Design (ISPD), Taipei, Mar. 12–15, 2024. (Best Paper Candidate)
(paper) (slides)
[C11] Shixin Chen, Su Zheng, Chen Bai, Wenqian Zhao, Shuo Yin, Yang Bai, Bei Yu, “SoC-Tuner: An Importance-guided Exploration Framework for DNN-targeting SoC Design”, IEEE/ACM Asian and South Pacific Design Automation Conference (ASPDAC), South Korea, Jan. 22–25, 2024.
(paper) (slides)
[C10] Chen Bai, Xuechao Wei, Youwei Zhuo, Yi Cai, Hongzhong Zheng, Bei Yu, Yuan Xie, “Klotski: DNN Model Orchestration Framework for Dataflow Architecture Accelerators”, IEEE/ACM International Conference on Computer-Aided Design (ICCAD), San Francisco, Oct. 29–Nov. 02, 2023. (paper) (slides) (poster) (video)
[C9] Ziyang Yu, Chen Bai, Shoubo Hu, Ran Chen, Taohai He, Mingxuan Yuan, Bei Yu, Martin Wong, “IT-DSE: Invariant Risk Minimized Transfer Microarchitecture Design Space Exploration”, IEEE/ACM International Conference on Computer-Aided Design (ICCAD), San Francisco, Oct. 29–Nov. 02, 2023. (paper) (slides)
[C8] Chen Bai, Jiayi Huang, Xuechao Wei, Yuzhe Ma, Sicheng Li, Hongzhong Zheng, Bei Yu, Yuan Xie, “ArchExplorer: Microarchitecture Exploration Via Bottleneck Analysis”, IEEE/ACM International Symposium on Microarchitecture (MICRO), Toronto, Oct. 28–Nov. 01, 2023.
(paper) (slides) (code) (poster)
[C7] Chen Bai*, Sicheng Li*, Xuechao Wei, Bizhao Shi, Yen-Kuang Chen, Yuan Xie, “2022 ICCAD CAD Contest Problem C: Microarchitecture Design Space Exploration”, IEEE/ACM International Conference on Computer-Aided Design (ICCAD), San Diego, Oct. 30–Nov. 3, 2022.
(Invited Paper) (paper) (slides) (code)
[C6] Ziyi Wang, Chen Bai, Zhuolun He, Guangliang Zhang, Qiang Xu, Tsung-Yi Ho, Bei Yu, Yu Huang, “Functionality Matters in Netlist Representation Learning”, ACM/IEEE Design Automation Conference (DAC), San Francisco, CA, Jul. 10–14, 2022. (paper) (slides)
[C5] Qi Sun, Chen Bai, Tinghuan Chen, Hao Geng, Xinyun Zhang, Yang Bai, Bei Yu, “Fast and Efficient DNN Deployment via Deep Gaussian Transfer Learning”, IEEE International Conference on Computer Vision (ICCV), Oct. 11-17, 2021. (paper) (slides) (poster)
[C4] Zhuolun He, Ziyi Wang, Chen Bai, Haoyu Yang, Bei Yu, “Graph Learning-Based Arithmetic Block Identification”, IEEE/ACM International Conference on Computer-Aided Design (ICCAD), Nov. 1–4, 2021. (paper) (slides)
[C3] Chen Bai, Qi Sun, Jianwang Zhai, Yuzhe Ma, Bei Yu, Martin D.F. Wong, "BOOM-Explorer: RISC-V BOOM Microarchitecture Design Space Exploration Framework", IEEE/ACM International Conference on Computer-Aided Design (ICCAD), Nov. 1–4, 2021.
(William J. McCalla Best Paper Award) (paper) (slides) (code) (video)
[C2] Jianwang Zhai, Chen Bai, Binwu Zhu, Yici Cai, Qiang Zhou, Bei Yu, “McPAT-Calib: A Microarchitecture Power Modeling Framework for Modern CPUs”, IEEE/ACM International Conference on Computer-Aided Design (ICCAD), Nov. 1–4, 2021. (paper) (slides)
[C1] Qi Sun, Chen Bai, Hao Geng, Bei Yu, "Deep Neural Network Hardware Deployment Optimization via Advanced Active Learning", IEEE/ACM Proceedings Design, Automation and Test in Europe (DATE), Feb. 01–05, 2021. (paper) (slides)
[J7] Wenqian Zhao, Shuo Yin, Chen Bai, Zixiao Wang, Bei Yu, “BAQE: Backend-Adaptive DNN Deployment via Synchronous Bayesian Quantization and Hardware Configuration Exploration”, accepted by IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD).
[J6] Chen Bai, Xuechao Wei, Youwei Zhuo, Yi Cai, Hongzhong Zheng, Bei Yu, Yuan Xie, “Klotski v2: Improved DNN Model Orchestration Framework for Dataflow Architecture Accelerators”, accepted by IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD). (paper)
[J5] Ziyi Wang, Chen Bai, Zhuolun He, Guangliang Zhang, Qiang Xu, Tsung-Yi Ho, Yu Huang, Bei Yu, “FGNN2: A Powerful Pre-training Framework for Learning the Logic Functionality of Circuits”, accepted by IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD). (paper) (code)
[J4] Chen Bai, Qi Sun, Jianwang Zhai, Yuzhe Ma, Bei Yu, Martin D.F. Wong, “BOOM-Explorer: RISC-V BOOM Microarchitecture Design Space Exploration”, accepted by ACM Transactions on Design Automation of Electronic Systems (TODAES). (paper)
[J3] Su Zheng, Hao Geng, Chen Bai, Bei Yu, Martin Wong, “Boosting VLSI Design Flow Parameter Tuning with Random Embedding and Multi-objective Trust-region Bayesian Optimization”, ACM Transactions on Design Automation of Electronic Systems (TODAES), vol. 28, no. 05, pp. 1–23, 2023. (paper)
[J2] Ziyi Wang, Zhuolun He, Chen Bai, Haoyu Yang, Bei Yu, “Efficient Arithmetic Block Identification with Graph Learning and Network-flow”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 42, no. 08, pp. 2591–2603, 2023. (paper)
[J1] Jianwang Zhai, Chen Bai, Binwu Zhu, Yici Cai, Qiang Zhou, Bei Yu, “McPAT-Calib: A RISC-V BOOM Microarchitecture Power Modeling Framework”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 42, no. 01, pp. 243–256, 2023.
(paper)
Chen Bai, “Trilogy of Microprocessor Microarchitecture Design Space Exploration: Delving into the Depths”, Ph.D. Dissertation at The Chinese University of Hong Kong, Jul. 2024. (paper)
Alibaba DAMO Academy, Beijing, P.R. China, Jun. 2022 - May. 2024
Research Intern, Computing Technology Lab
Topic: Chip agile design methodology & Next-generation computing substrate
Huawei Hong Kong Research Center, Hong Kong SAR, Jun. 2021 - Apr. 2022
Research Intern, Turing Core & Key Technologies Development Department, HiSilicon HK
Topic: Microprocessor design space exploration & Power modeling
SenseTime, Beijing, P.R. China, Sep. 2019 - Jul. 2020
Research Intern, Intelligent Video Generation Group
Topic: SenseAR DigitalHuman — Audio-Driven Virtual Human (China Daily)
Intel Asia-Pacific R. & D. Center, Shanghai, P.R. China, Feb. 2019 - Jul. 2019
Engineering Intern, Web Runtime Optimization Group
Topic: Chrome browser optimization for Intel architecture-based Chromebooks
University of Maryland, Washington, D.C., U.S.A., Jul. 2018 - Aug. 2018
Visiting Student, School of Public Policy
Topic: Study of "Leadership, Innovation, and Decision Making"
2020 Fall: ENGG1110E Problem Solving By Programming
ISPD Best Paper Candidate, IEEE CEDA and ACM SIGDA, 2024
ICCAD Student Scholar Program Travel Support Grant, Futurewei, IEEE CEDA, and ACM SIGDA, 2023
MICRO 2023 Student Travel Grants, TCuARCH and ACM SIGMICRO, 2023
William J. McCalla ICCAD Best Paper Award, The IEEE Council on Electronic Design Automation (IEEE CEDA) and the ACM Special Interest Group on Design Automation (ACM SIGDA), 2021
Full Postgraduate Scholarship, The Chinese University of Hong Kong, 2020 - 2024
Outstanding Graduate, The Education Department of Sichuan Province, 2020
Excellent Thesis Award, The University of Electronic Science and Technology of China, 2020
National Scholarship, Ministry of Education, 2017 - 2018
National Scholarship, Ministry of Education, 2016 - 2017
Meritorious Winner of Interdisciplinary Contest on Modeling (ICM), COMAP, INFORMS, SIAM, MAA, ASA, AMS, 2018
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD)
ACM Transactions on Design Automation of Electronic Systems (TODAES)
IEEE Transactions On Very Large Scale Integration (VLSI) Systems (TVLSI)
IEEE Design & Test (D&T)
2022, 2023, 2024 ACM/IEEE Design Automation Conference (DAC)
2022, 2024 ACM/IEEE International Conference on Computer-Aided Design (ICCAD)
2022, 2023 ACM/IEEE Asia and South Pacific Design Automation Conference (ASPDAC)
2022 ACM/IEEE Workshop on Machine Learning for CAD ((MLCAD)