About me

I am Qijun Zhang, a second-year PhD student at the Electronic and Computer Engineering Department of the Hong Kong University of Science and Technology (HKUST) advised by Prof. Zhiyao Xie. Before that, I received the bachelor degree in Computer Science in Tongji University in 2022. My research interests include computer architecture and electronic design automation.

Research Interests

  • Computer Architecture
  • Electronic Design Automation

Education

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

Publication

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

  • 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

  • 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).

  • Shang Liu, Wenji Fang, Yao Lu, Jing Wang, Qijun Zhang, Hongce Zhang, and Zhiyao Xie, “RTLCoder: Fully Open-Source and Efficient LLM-Assisted RTL Code Generation Technique”. In IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2025.

  • Wenji Fang, Yao Lu, Shang Liu, Qijun Zhang, Ceyu Xu, Lisa Wu Wills, Hongce Zhang, and Zhiyao Xie, “Transferable Pre-Synthesis PPA Estimation for RTL Designs with Data Augmentation Techniques”. In IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2025.

  • Shang Liu, Wenji Fang, Yao Lu, Qijun Zhang, and Zhiyao Xie, “Towards Big Data in AI for EDA Research: Generation of New Pseudo Circuits at RTL Stage”. In Asia and South Pacific Design Automation Conference (ASP-DAC 2025).

  • Shang Liu, Wenji Fang, Yao Lu, Qijun Zhang, Hongce Zhang, and Zhiyao Xie, “RTLCoder: Outperforming GPT-3.5 in Design RTL Generation with Our Open-Source Dataset and Lightweight Solution”. In IEEE International Workshop on LLM-Aided Design (LAD 2024). Best Paper Nomination

  • Yao Lu, Shang Liu, Qijun Zhang, and Zhiyao Xie, “RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Model”. In Asia and South Pacific Design Automation Conference (ASP-DAC 2024).

  • Wenji Fang, Yao Lu, Shang Liu, Qijun Zhang, Ceyu Xu, Lisa Wu Wills, Hongce Zhang, and Zhiyao Xie, “MasterRTL: A Pre-Synthesis PPA Estimation Framework for Any RTL Design”. In IEEE/ACM International Conference on Computer Aided Design (ICCAD 2023).