Research Topics

Topic 1: Deep Neural Network Design Automation

In this project we will explore 1) DNN compression / acceleration and 2) DNN design space exploration.


Selected recent publications:

  • [C114] Qi Sun, Tinghuan Chen, Siting Liu, Jin Miao, Jianli Chen, Hao Yu, Bei Yu, “Correlated Multi-objective Multi-fidelity Optimization for HLS Directives Design”, IEEE/ACM Proceedings Design, Automation and Test in Europe (DATE), Feb. 01–05, 2021. (paper) (slides) (Best Paper Award Nomination)

  • [C113] 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)

Topic 2: Deep Mask Learning


Selected recent publications:

  • [C104] Guojin Chen, Wanli Chen, Yuzhe Ma, Haoyu Yang, Bei Yu, “DAMO: Deep Agile Mask Optimization for Full Chip Scale”, IEEE/ACM International Conference on Computer-Aided Design (ICCAD), Nov. 2–5, 2020. (paper) (slides) (whova)

  • [C57] Hang Zhang, Fengyuan Zhu, Haocheng Li, Evangeline F. Y. Young, Bei Yu, “Bilinear Lithography Hotspot Detection”, ACM International Symposium on Physical Design (ISPD), pp. 7–14, Portland, OR, Mar. 19–22, 2017. (paper) (Best Paper Award)

Topic 3: Learning on Chips

Machine learning is a powerful computer science technique which can derive knowledge from big data, and provides prediction and matching. Since nanometer VLSI CAD problems have extremely high complexity and gigantic data, there has been a surge recently in applying and adapting machine learning techniques in VLSI CAD.


Selected recent publications:

Topic 4: Hardware Friendly Computer Vision


Topic 5: Combinatorial Algorithms in VLSI CAD

Many classical VLSI CAD problems can be extracted and formulated into challenging combinatorial optimization problems. We are heavily working to improve the state-of-the-art.


Selected recent publications are listed as follows: