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Jiawei Liang

I’m currently a PhD student superivised by Professor Wei Zhang in Reconfigurable computing system laboratory (RCSL) at the Department of Electronic & Computer Engineering in HKUST. My research interests focus on hardware system and EDA for FPGA. Previously, I obtained BEng and MSc Degree in school of EE from Beihang University, China, working for CNN pruning at that time. Later, I served as an IC designer for 2 years.


Experience

Education Experience

HongKong University of Science and Technology, HongKong, China.

(2024.01 - )

Doctor of Philosophy (Ph.D.) in Electronic and Computer Engineering (ECE).

Beihang University, Beijing, China.

(2019.09 - 2022.01)

Master of Research in Electronic and Information Engineering (EE).

Beihang University, Beijing, China.

(2015.09 - 2019.07)

Bachelor of Engineering (B.Eng.) in Electronic and Information Engineer (EE).

Industrial Experience

Zeku Tech., Beijing, China.

(2022.02 - 2023.05)

NVIDIA Tech., Beijing, China.

(2021.07 - 2021.09)

Teaching Experience

ELEC 4320, FPGA-based Design: From Theory to Practice, Autumn

School of Engineering, Department of ECE, HKUST. (2024.09 - )

Digital Signal Processing, Autumn

Department of EE, BUAA. (2020.09 - 2021.01)

Electronic Design, Summer

Department of EE, BUAA. (2020.07 - 2020.09)


Publication

Google Scholar

Journal

[J2] He Li, Jiawei Liang, Hongxiang Fan, “Design space exploration for efficient quantum most-significant digit-first arithmetic”, to appear in the IEEE Transactions on Computers (TC), 2022. pdf code

[J1] Pei Lei, Jiawei Liang, Tong Zheng, Jun Wang, “Design space exploration for efficient quantum most-significant digit-first arithmetic”, to appear in the IEEE Transactions on Neural Network and Learning System (TNNLS), 2022. pdf

Conference

[C3] Jiawei Liang, Linfeng Du, Xiaofeng Zhou, Zhe Lin, Jiang Xu, Wei Zhang, “AutoClock: Automated Clock Management for Power-Efficient HLS Designs on FPGAs”, 62th ACM/IEEE Design Automation Conference (DAC), 2025. code

[C2] Yipu Zhang, Jiawei Liang, Jian Peng, Jiang Xu, Wei Zhang, “SpNeRF: Memory Efficient Sparse Volumetric Neural Rendering Accelerator for Edge Devices”, Design, Automation & Test in Europe Conference & Exhibition (DATE), 2025.

[C1] He Li, Hongxiang Fan, Jiawei Liang, “Quantum most-significant digit-first addition”, 12th International Green and Sustainable Computing Conference (IGSC), 2021. pdf