Qijun Zhang

Qijun Zhang

Ph.D. Student, ECE, HKUST

About

I am Qijun Zhang, a third-year Ph.D. student in the Department of Electronic and Computer Engineering at the Hong Kong University of Science and Technology (HKUST), advised by Prof. Zhiyao Xie. Before joining HKUST, I received my B.Eng. in Computer Science from Tongji University in 2022.

My research lies at the intersection of computer architecture and electronic design automation, with a particular focus on architecture-level power modeling and scale-up interconnect architecture. My work has appeared in ISCA, HPCA, DAC, ICCAD, ASP-DAC, TCAD, and NeurIPS. A complete list of publications is available on my Publications page and Google Scholar.

Education

  • Ph.D. in Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Aug. 2023 - present
  • B.Eng. in Computer Science, Tongji University, Sep. 2018 - Jul. 2022

Research Interests

  • Architecture-level power modeling
  • Scale-up interconnect architecture

Selected Publications

  • Qijun Zhang, Chen Zhang, Zhuoshan Zhou, Haibo Wang, Zhe Zhou, Zhipeng Tu, Guangyu Sun, Zhiyao Xie, Yijia Diao, Zhigang Ji, Jingwen Leng, Guanghui He, and Minyi Guo, “Tackling MoE Communication Bottleneck with Dynamic In-Switch Computing on Multi-GPUs”. In 53rd Annual International Symposium on Computer Architecture (ISCA 2026).

  • Qijun Zhang, Yao Lu, Shang Liu, Mengming Li, Chen Zhang, Dongbo Wang, and Zhiyao Xie, “G-Power: Architecture-level GPU Power Modeling with Aggregated Knowledge Foundations from Known GPUs”. In ACM/IEEE Design Automation Conference (DAC 2026).

  • Chen Zhang, Qijun Zhang†, Zhuoshan Zhou, Yijia Diao, Haibo Wang, Zhe Zhou, Zhipeng Tu, Zhiyao Li, Guangyu Sun, Zhuoran Song, Zhigang Ji, Jingwen Leng, and Minyi Guo, “Towards Compute-Aware In-Switch Computing for LLMs Tensor-Parallelism on Multi-GPU Systems”. In 32nd International Symposium on High Performance Computer Architecture (HPCA 2026). († Corresponding Author)

  • Qijun Zhang, Shang Liu, Yao Lu, Mengming Li, and Zhiyao Xie, “ReadyPower: A Reliable, Interpretable, and Handy Architectural Power Model Based on Analytical Framework”. In Asia and South Pacific Design Automation Conference (ASP-DAC 2026).

  • Qijun Zhang, Yao Lu, Mengming Li, Shang Liu, and Zhiyao Xie, “ArchPower: Dataset for Architecture-Level Power Modeling of Modern CPU Design”. In 39th Annual Conference on Neural Information Processing Systems (NeurIPS 2025).

  • Qijun Zhang, Yao Lu, Mengming Li, and Zhiyao Xie, “AutoPower: Automated Few-Shot Architecture-Level Power Modeling by Power Group Decoupling”. In ACM/IEEE Design Automation Conference (DAC 2025).

  • Mengming Li*, Qijun Zhang*, Yongqing Ren, and Zhiyao Xie, “Integrating Prefetcher Selection with Dynamic Request Allocation Improves Prefetching Efficiency”. In 31st IEEE International Symposium on High-Performance Computer Architecture (HPCA 2025). (* Equal Contribution)

  • Qijun Zhang, Mengming Li, Andrea Mondelli, and Zhiyao Xie, “An Architecture-Level CPU Modeling Framework for Power and Other Design Qualities”. In IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2025.

  • Qijun Zhang, Mengming Li, Yao Lu, and Zhiyao Xie, “FirePower: Towards a Foundation with Generalizable Knowledge for Architecture-Level Power Modeling”. In Asia and South Pacific Design Automation Conference (ASP-DAC 2025).

  • Qijun Zhang, and Zhiyao Xie, “Pointer: An Energy-Efficient ReRAM-based Point Cloud Recognition Accelerator with Inter-layer and Intra-layer Optimizations”. In Asia and South Pacific Design Automation Conference (ASP-DAC 2025).

  • Yao Lu*, Qijun Zhang*, and Zhiyao Xie, “Unleashing Flexibility of ML-based Power Estimators Through Efficient Development Strategies”. In ACM/IEEE International Symposium on Low Power Electronics and Design (ISLPED 2024). Best Paper Nomination. (* Equal Contribution)

  • Qijun Zhang, Shiyu Li, Guanglei Zhou, Jingyu Pan, Chen-Chia Chang, Yiran Chen, and Zhiyao Xie, “PANDA: Architecture-Level Power Evaluation by Unifying Analytical and Machine Learning Solutions”. In IEEE/ACM International Conference on Computer Aided Design (ICCAD 2023).

Honors and Awards

  • RedBird PhD Award for Continuing PhD Students, HKUST, 2025
  • ACM/IEEE ISLPED 2024 Best Paper Nomination, 2024
  • IEEE LAD 2024 Best Paper Nomination, 2024
  • RedBird PhD Award for New PhD Students, HKUST, 2023
  • Full Postgraduate Studentship, HKUST, 2023 - 2027
  • Excellent Graduates, Tongji University, 2022
  • 2nd Prize of Outstanding Student Scholarship, Tongji University, 2021
  • National Scholarship, 2020
  • 1st Prize of Outstanding Student Scholarship, Tongji University, 2019