Zhan Shi

Zhan Shi

Zhan Shi

Research Associate
Department of Computer Science
University of Texas at Austin (UT Austin)
zshi17@cs.utexas.edu

I’m currently a research associate at the University of Texas at Austin (UT Austin), where I completed my Ph.D degree in 2020 under the supervision of Professor Calvin Lin. I am interested in the interplay between machine learning (ML) and computer systems, with a particular focus on ML for systems. My research develops principled data-driven methods for system optimization problems, including learning-based cache optimizations for general-purpose CPUs and data-driven design automation tooling/EDA for specialized AI accelerators. I serve in the program committees of ML for Systems workshop and serve as a reviewer for IEEE MICRO. During my Ph.D., I have interned at Google (Summer 2018 and 2019) and Facebook (Summer 2020).

Publications

[HPCA] Leveraging Domain Information for the Efficient, Automated Design of Deep Learning Accelerators
Chirag Sakhuja*, Zhan Shi*, Calvin Lin
International Symposium on High-Performance Computer Architectural (HPCA), 2023

[MICRO] Heterogeneity-Aware Hierarchical Management for Federated Learning System
Chunlin Tian*, Li Li*, Zhan Shi*, Jun Wang, ChengZhong Xu
International Symposium on Microarchitecture (MICRO), 2022

[ASPLOS] A Hierarchical Neural Model of Data Prefetching
Zhan Shi, Akanksha Jain, Kevin Swersky, Parthasarathy Ranganathan, Milad Hashemi, Calvin Lin
International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) 2021
IEEE Micro Top Picks Honorable Mention 2022

Learned Hardware/Software Co-Design of Neural Accelerators
Zhan Shi, Chirag Sakhuja, Milad Hashemi, Kevin Swersky, Calvin Lin
Workshop on ML for Systems at NeurIPS (MLFS), 2020
arXiv:2010.02075, 2021

Machine Learning for Prediction Problems in Computer Architecture
Zhan Shi
Ph.D. Dissertation, 2020

[ICLR] Learning Execution through Neural Code Fusion
Zhan Shi, Kevin Swersky, Danny Tarlow, Parthasarathy Ranganathan, Milad Hashemi
International Conference on Learning Representations (ICLR), 2020

[MICRO] Applying Deep Learning to the Cache Replacement Problem
Zhan Shi, Xiangru Huang, Akanksha Jain, Calvin Lin
International Symposium on Microarchitecture (MICRO), 2019

[AAAI] Deep Embedding for Determining the Number of clusters
Yiqi Wang*, Zhan Shi*, Xifeng Guo, Xinwang Liu, En Zhu and Jianping Yin
Student Abstract in AAAI Conference on Artificial Intelligence (AAAI), 2018

Awards and Honors

  • Honorable Mention, IEEE Micro Top Picks, 2022.
  • Provost’s Graduate Excellence Fellowship, Graduate School, University of Texas at Austin, 2017-2021
  • Funded by Faculty Research Award, Google, 2020
  • Funded by Faculty Research Award, Google, 2019
  • China Science and Technology Corporation Fellowship, 2016
  • ASC First Class Prize, ASC Supercomputer Challenge, 2016