Customizable computing with software-defined hardware acceleration:

• Hardware/software codesign for machine learning: especially for accelerating early-day CNNs, sub-8-bit mixed-precision CNNs, vision transformers, stable diffusion, low-bit training, and SNNs
• Hardware/software codesign for big data analytics: especially for accelerating structured queries, vector database queries, and common big data processing tax
• Other workload characterization and acceleration: especially for precision medicine, high-performance computing, quantum chemistry, fully homomorphic encryption, and learned image processing
• Characterization of commodity hardware platforms
• Next-generation computer architecture: especially for heterogeneous and energy-efficient accelerator-rich architectures, memory systems and near data acceleration, and 3D FPGA
• Programming, automation, and compiler support: especially support for the above computer architectures
• Reliability for hardware accelerators: especially for learning-based hardware reliability modeling, and robustness for machine learning accelerators

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Education

• 7/2014 – 9/2017 University of California, Los Angeles, USA, Postdoctoral Scholar, Computer Science. Supervisor: Prof. Jason Cong and Prof. Glenn Reinman

• 4/2013 – 7/2014 University of Minnesota, Twin Cities, USA, Visiting PhD, Computer Science and Engineering. Supervisor: Prof. Pen-Chung Yew

• 9/2009 – 6/2014 Fudan University, China, PhD, Computer Science. Supervisor: Prof. Binyu Zang

• 9/2005 – 7/2009 Fudan University, China and University College Dublin, Ireland, Joint Bachelor’s Degree. 
  - Bachelor of Engineering, Software Engineering, Fudan University (Outstanding Graduate)
  - Bachelor of Science Honors, Computer Science, University College Dublin

Professional Background

• 4/2019 – present Simon Fraser University, Burnaby, BC, Canada. Assistant Professor and then Associate Professor (On Leave)
• 9/2017 – 3/2019 Xilinx, San Jose, CA, USA. Staff Software Engineer, SDx (now re-branded as AMD Vitis) System Compiler Team 

Honors and Awards

• ICCAD 10 Year Retrospective Most Influential Paper Award in 2025
• ICCAD 2025 Best Paper Nominee
• FCCM 2025 Best Paper Nominee
• Inaugural Research Excellence Award - Horizon Award, Simon Fraser University, 2025
• FPL 2024 Stamatis Vassiliadis Best Paper Award
• FPGA 2021 Paper Highlighted in ACM TRETS Special Issue
• Natural Sciences and Engineering Research Council of Canada (NSERC) Alliance Award, 2020
• Canada Foundation for Innovation John R. Evans Leaders Fund (CFI JELF) Award, 2019
• TCAD 2019 Donald O. Pederson Best Paper Award
• ISPASS 2018 Best Paper Nominee
• MEMSYS 2017 Best Paper Award
• HPCA 2017 Best Paper Nominee
• Postdoc Fellowship, UCLA Institute for Digital Research and Education, 2016

Selected Publications

• Qitao Tan, Sung-En Chang, Rui Xia, Huidong Ji, Chence Yang, Ci Zhang, Jun Liu, Zheng Zhan, Zhenman Fang, Zhuo Zou, Yanzhi Wang, Jin Lu and Geng Yuan. Perturbation-efficient Zeroth-order Optimization for Hardware-friendly On-device Training. The 2025 IEEE/ACM International Conference On Computer Aided Design (ICCAD 2025 Best Paper Nominee), Munich, Germany, Oct 2025, pp. 1-9.
• Dilshan Sampath, Qilin Hu, Zhenman Fang. AutoNTT: Automatic Architecture Design and Exploration for Number Theoretic Transform Acceleration on FPGAs. The 33rd IEEE International Symposium On Field-Programmable Custom Computing Machines (FCCM 2025 Best Paper Nominee), Fayetteville, AR, May 2025, pp. 1-9.
• Philip Stachura, Guanyu Li, Xin Wu, Christian Plessl, Zhenman Fang. SERI: High-Throughput Streaming Acceleration of Electron Repulsion Integral Computation in Quantum Chemistry using HBM-based FPGAs. The 34th IEEE International Conference on Field-Programmable Logic and Applications (FPL 2024 Stamatis Vassiliadis Best Paper), Turin, Italy, September 2024, pp. 60-68.
• Alec Lu, Zhenman Fang, and Lesley Shannon. Demystifying the Soft and Hardened Memory Systems of Modern FPGAs for Software Programmers through Microbenchmarking. ACM Transactions on Reconfigurable Technology and Systems (TRETS 2022 Special Issue on FPGA 2021 Highlights), Volume 15, Issue 4, December 2022, Article No.: 43, pp 1–33.
• Chen Zhang, Guangyu Sun, Zhenman Fang, Peipei Zhou, Peichen Pan, Jason Cong. Caffeine: Towards Uniformed Representation and Acceleration for Deep Convolutional Neural Networks. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD 2019 Donald O. Pederson Best Paper), Volume 38, Issue 11, Pages 2072-2085, Nov. 2019.
• Peipei Zhou, Zhenyuan Ruan, Zhenman Fang, Jason Cong, Megan Shand, and David Roazen. Doppio: I/O-Aware Performance Analysis, Modeling and Optimization for In-Memory Computing Framework. The 2018 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS 2018 Best Paper Nominee), Belfast, Northern Ireland, UK, Apr 2018, pp. 22-32.
• Jason Cong, Zhenman Fang, Michael Gill, Farnoosh Javadi, Glenn Reinman. AIM: Accelerating Computational Genomics through Scalable and Noninvasive Accelerator-Interposed Memory. The ACM International Symposium on Memory Systems (MEMSYS 2017 Best Paper), Alexandria, VA, Oct 2017, pp. 3-14.
• Jason Cong, Zhenman Fang, Yuchen Hao, Glenn Reinman. Supporting Address Translation for Accelerator-Centric Architectures. The 23rd IEEE Symposium on High Performance Computer Architecture (HPCA 2017 Best Paper Nominee), Austin TX, Feb 2017, pp. 37-48.
• Chen Zhang, Zhenman Fang, Peipei Zhou, Peichen Pan, Jason Cong. Caffeine: Towards Uniformed Representation and Acceleration for Deep Convolutional Neural Networks. The 2016 IEEE/ACM International Conference on Computer-Aided Design (ICCAD 2016), Austin TX, Nov 2016, pp. 1-8. This paper has received the ICCAD 10 Year Retrospective Most Influential Paper Award in 2025. Its journal extension has received the TCAD 2019 Donald O. Pederson Best Paper Award.