Zhenman Fang
Assistant Professor

Computer Engineering Option
School of Engineering Science
Simon Fraser University
8888 University Drive | ASB 9837
Burnaby, BC V5A 1S6, Canada

Email: zhenman [at] sfu [dot] ca

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News

  • Dr. Fang is actively looking for PhD and Master students to join his HiAccel lab. Please find more details in the recruiting page. For SFU students who are interested in joining HiAccel lab, the best way is to take the ENSC 453/894 course in Fall (used to be ENSC 462 in Summer) on "Programming for Heterogeneous Computing System" and stand out in the class.
    • Here is a nice, high-level introduction to FPGAs, one of the key components of our research: Architecture All Access: Modern FPGA Architecture. This talk is given by Intel, but most of the contents apply to both Intel and Xilinx FPGAs.
  • May 2023, congratulations to William for winning the BC Graduate Scholarship!
  • May 2023, Dr. Fang chaired the Applications/ML session, in-person poster session, and demo night at FCCM 2023!
  • May 2023, Dr. Fang is invited to serve on the FPT 2023 TPC, welcome to submit!
  • Apr 2023, Dr. Fang is elevated to IEEE Senior Member!
  • Apr 2023, Dr. Fang is invited to serve on the MICRO 2023 TPC (Light Load), welcome to submit!
  • Mar 2023, two full papers accepted by FCCM 2023. One paper is on "PASTA: Programming and Automation Support for Scalable Task-Parallel HLS Programs on Modern Multi-Die FPGAs", congrats to Moazin and Xingyu! This is a work in collaboration with Prof. Jason Cong's group at UCLA and led by SFU. The other paper is on "SQL2FPGA: Automatic Acceleration of SQL Query Processing on Modern CPU-FPGA Platforms", congrats to Alec!
  • Mar 2023, one paper accepted by TGRS. The paper is on "SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery". This is a work in collaboration with Prof. Jie Lei's group from Xidian University (lead).
  • Feb 2023, Dr. Fang gives a talk on "HeatViT: Hardware-Efficient Adaptive Token Pruning for Vision Transformers" at HPCA 2023!
  • Feb 2023, Dr. Fang gives a talk on "SASA: A Scalable and Automatic Stencil Acceleration Framework for Optimized Hybrid Spatial and Temporal Parallelism on HBM-based FPGAs" at the LATHC 2023 workshop, co-located with CGO 2023!
  • Feb 2023, we have open sourced our SASA framework that supports scalable and automatic stencil acceleration on HBM-based FPGAs. Check it out here.
  • Feb 2023, Dr. Fang is invited to give a talk on "Hardware/Software Codesign to Accelerate Vision Transformers on FPGAs" at the AAAI-EMC2 2023 workshop!
  • Feb 2023, Dr. Fang is invited to serve on the FCCM 2023 Demo Night Co-Chair, welcome to submit your demo proposals!
  • Dec 2022, Dr. Fang is invited to serve on the FPL 2023 TPC, welcome to submit!
  • Dec 2022, Dr. Fang is invited to serve on the FCCM 2023 TPC, welcome to submit!
  • Nov 2022, Dr. Fang is on the organizing committee of the first Languages, Architectures, and Tools for Heterogeneous Computing (LATHC) Workshop, co-located with CGO 2023 in Montreal. Welcome to submit your talk abstract, before Jan 6, 2023.
  • Nov 2022, one paper accepted by DATE 2023. This paper is our second effort on low-precision DNN training "ESRU: Extremely Low-Bit and Hardware-Efficient Stochastic Rounding Unit Design for 8-Bit DNN Training", congrats to Alec. This is a work in collaboration with Prof. Yanzhi Wang's group from Northeastern University (lead).
  • Nov 2022, one paper accepted by TRETS. The paper is on "SASA: A Scalable and Automatic Stencil Acceleration Framework for Optimized Hybrid Spatial and Temporal Parallelism on HBM-based FPGAs". Congrats to Xingyu, Alec, and our Mitacs intern Zhifan!
  • Nov 2022, one paper accepted by TETC. The is a collaboration paper with UCLA team (lead) on "TopSort: A High-Performance Two-Phase Sorting Accelerator Optimized on HBM-based FPGAs"!
  • Oct 2022, Dr. Fang is invited to serve on the DAC 2023 TPC, welcome to submit!
  • Oct 2022, one paper accepted by HPCA 2023. This paper is our second effort on Vision Transformer (ViT) acceleration "HeatViT: Hardware-Efficient Adaptive Token Pruning for Vision Transformers", congrats to Alec and Kenny. This is a work in collaboration with Prof. Yanzhi Wang's and Prof. Xue Lin's group from Northeastern University (lead).
  • Sept 2022, four MASc students join HiAccel lab! Welcome, Abdul, Dilshan, Manoj, and William (Accelerated MASc)!
  • Sept 2022, Dr. Fang is invited to serve on the ISCA 2023 ERC, welcome to submit!
  • Aug 2022, Mitacs Globalink Research Internship positions available in the HiAccel team for Summer 2023. The project is for international senior undergraduate students. Please apply by September 22, 2022 at 1 pm PT. One project will be on "Big Data Acceleration on FPGAs". More details on the project can be found here. Just filter the projects with Host University as "Simon Fraser University - Burnaby" and Professor's Last Name as "Fang"!
  • Jul 2022, one paper accepted by ECCV 2022. This paper is our first effort on low-precision DNN training "You Already Have It: A Generator-Free Low-Precision DNN Training Framework using Stochastic Rounding", congrats to Alec. This is a work in collaboration with Prof. Yanzhi Wang's group from Northeastern University (lead).
  • Jul 2022, Dr. Fang is invited to serve on the FPGA 2023 TPC, welcome to submit!
  • Jun 2022, two papers accepted by Euromicro DSD 2022. The papers are on "Blind Data Adversarial Bit-flip Attack against Deep Neural Networks" and "A Majority-based Approximate Adder for FPGAs", congrats to Behnam (Research Associate, co-supervised with Prof. Lesley Shannon)!
  • Jun 2022, Dr. Fang is hosting a virtual office hour at Undergrad Architecture Mentoring (uArch) Workshop 2022, co-located with ISCA 2022, welcome to attend!
  • Jun 2022, one paper accepted by FPL 2022. This paper is our first effort on Vision Transformer (ViT) acceleration "Auto-ViT-Acc: FPGA-Aware Automatic Acceleration Framework for Vision Transformer with Mixed-Scheme Quantization", congrats to Alec. This is a work in collaboration with Prof. Xue Lin's group from Northeastern University (lead).
  • More News

Bio

Dr. Zhenman Fang is a Tenure-Track Assistant Professor in School of Engineering Science (Computer Engineering Option) and an Associate Member in School of Computing Science, Simon Fraser University, Canada. Zhenman founded and directs the HiAccel lab. He is also a member of SFU Systems Group.

Zhenman's recent research focuses on customizable computing with specialized hardware acceleration, which aims to sustain the ever-increasing performance, energy-efficiency, and reliability demand of important application domains in post-Moore’s law era. It spans the entire computing stack, including emerging application characterization and acceleration (including machine learning, computational genomics, big data analytics, and high-performance computing), novel accelerator-rich and near-data computing architecture designs, and corresponding programming, runtime, and tool support. Zhenman has published over 50 papers in top conferences and journals and two US patents, including two best paper awards (TCAD 2019 Donald O. Pederson best paper award and MEMSYS 2017), two best paper nominees (HPCA 2017 and ISPASS 2018), and an invited paper from Proceedings of the IEEE 2019. His research has also been recognized with a NSERC (Natural Sciences and Engineering Research Council of Canada) Alliance Award (2020), a CFI JELF (Canada Foundation for Innovation John R. Evans Leaders Fund) Award (2019), a Xilinx University Program Award (2019), a Team Award from Xilinx Software and IP Group (2018), and a Postdoc Fellowship from UCLA Institute for Digital Research and Education (2016-2017).

Before joining SFU, Zhenman worked in the Xilinx SDx (now rebranded as AMD/Xilinx Vitis) group at San Jose as a Staff Software Engineer from Sept 2017 to Mar 2019. At Xilinx, he worked on the topic of accelerator-rich architectures and systems, which is the major focus of his postdoc research at UCLA. From Jul 2014 to Sept 2017, Zhenman was a postdoc in Department of Computer Science, UCLA, under the supervision of Prof. Jason Cong and Prof. Glenn Reinman. While at UCLA, he was also a member of the NSF/Intel funded multi-university Center for Domain-Specific Computing (CDSC) and SRC/DARPA funded multi-university Center for Future Architectures Research (C-FAR). Zhenman earned his Ph.D degree in Jun 2014 from School of Computer Science, Fudan University, China, under the supervision of Prof. Binyu Zang. He also spent the last 15 months of his PhD study visiting Department of Computer Science and Engineering, University of Minnesota at Twin Cities, under the supervision of Prof. Pen-Chung Yew.

Zhenman is a Professional Engineer in the province of British Columbia (registered with APEGBC) and a Senior Member of IEEE.

Recent Services

  • Organizing: FCCM 2023 Demo Night Co-Chair, FPL 2022 Publicity Chair, FCCM 2022 Workshop Chair and Travel Awards Chair, DAC-ROAD4NN 2020-2023 Organizing Chair, ASAP 2021 Special Session Chair
  • 2023 TPC: DAC, FPGA, FCCM, FPL, FPT, HPCA, ISCA (ERC), MICRO (Light Load)
  • 2022 TPC: DAC, DATE, FPGA, FCCM, FPL, FPT, MICRO, ISCA (ERC)
  • 2021 TPC: DAC, DATE, FPGA, IPDPS, ICCD, FPT, ASAP, ISCA (ERC), MICRO (ERC)
  • More Services

Research Interests

  • Emerging workload characterization and acceleration: especially for computational genomics, big data analytics, machine learning, image and video processing, and high-performance computing
  • Computer architecture: especially for heterogeneous and energy-efficient accelerator-rich architectures (ARAs), multicore and many-core architectures, memory systems and near data acceleration
  • Programming and compiler optimization: especially for improving memory system performance, programming and compiler support for the above architectures
  • Big data computing system: especially for enabling FPGA accelerators in datacenter
  • Performance evaluation and design automation: especially for architectural simulation, benchmarking, prototyping, and GPU-FPGA comparison
  • Reliability for hardware accelerators: especially for machine learning based reliability modeling for accelerators, and reliability for machine learning accelerators

Awards

Oct 2020
Natural Sciences and Engineering Research Council of Canada (NSERC) Alliance Award
Oct 2019
Xilinx University Program Award
Sept 2019
Canada Foundation for Innovation John R. Evans Leaders Fund (CFI JELF) Award
May 2019
TCAD 2019 Donald O. Pederson Best Paper Award, IEEE Council on Electronic Design Automation
Dec 2018
Team Award in Xilinx Software and IP Group (1000+ people group)
Jul 2018
Outstanding Reviewer, Elsevier Journal of Parallel and Distributed Computing (JPDC)
Apr 2018
ISPASS 2018 Best Paper Nominee
Oct 2017
MEMSYS 2017 Best Paper Award
Aug 2017
Outstanding Reviewer, Integration, the Elsevier VLSI Journal
Aug 2017
Outstanding Reviewer, Elsevier Microprocessors and Microsystems (MICPRO)
Feb 2017
HPCA 2017 Best Paper Nominee
Dec 2016
Best Demo Award (3rd Place out of 49 Demos) at C-FAR 2016 Annual Review
Jun 2016
Outstanding Reviewer, Elsevier Journal of Parallel and Distributed Computing (JPDC)
May 2016
UCLA Institute for Digital Research and Education (IDRE) 2016-2017 Postdoc Fellowship
Nov 2012
China National Scholarship for PhD Students, Ministry of Education of China