Assistant Professor, School of Engineering Science


Postdoctoral Scholar: Computer Science, University of California, Los Angeles, USA, 2017

Ph.D., Computer Science, Fudan University, China, 2014

Visiting Ph.D., Computer Science, University of Minnesota, Twin Cities, USA, 2014

B.Sc. Honors, Computer Science, University College Dublin, Ireland, 2009

B.Eng., Software Engineering, Fudan University, China, 2009

Research interests

  • Emerging workload characterization and optimization: especially for computational genomics, machine learning, and big data analytics.
  • Computer architecture: especially for heterogeneous and energy-efficient accelerator-rich architectures (ARAs), multicore and many-core architectures, memory systems and near data computing
  • Compiler optimization: especially for improving memory system performance, 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

Teaching interests

  • Computer Architecture and Organization
  • Programming for Heterogeneous Systems
  • Hardware/Software Codesign


Burnaby Campus, ASB 9837
Personal Webpage: http://www.sfu.ca/~zhenman
Group Webpage: 

Recently taught courses

  • ENSC 453/ENSC 894: Programming for Heterogenous Computing Systems
  • ENSC 254: Introduction to Computer Organization
  • ENSC 251: Software Design and Analysis for Engineers

Selected recent publications

For latest publications, please refer to Dr. Fang's website.

  • Yi-Hsiang Lai, Ecenur Ustun, Shaojie Xiang, Zhenman Fang, Hongbo Rong, Zhiru Zhang. Programming and Synthesis for Software-defined FPGA Acceleration: Status and Future Prospects. ACM Transactions on Reconfigurable Technology and Systems (TRETS 2021), Volume 14, Issue 4, December 2021, Article No.: 17, pp 1–39
  • 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 Best Paper), Volume 38, Issue 11, Pages 2072 - 2085, Nov. 2019.
  • Jason Cong, Zhenman Fang, Muhuan Huang, Peng Wei, Di Wu, Cody Hao Yu. Customizable Computing: From Single-Chip to Datacenters. Proceedings of the IEEE (PIEEE 2019), Volume 107, Issue 1, Pages 185 - 203, Jan. 2019).
  • Peipei Zhou, Zhenyuan Ruan, Zhenman Fang, Jason Cong, Megan Shand, 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 International Symposium on Memory Systems (MEMSYS 2017 Best Paper Award), 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.
  • Muhuan Huang, Di Wu, Cody Hao Yu, Zhenman Fang, Matteo Interlandi, Tyson Condie, Jason Cong. Programming and Runtime Support to Blaze FPGA Accelerator Deployment at Datacenter Scale. The ACM Symposium on Cloud Computing (ACM SoCC 2016), Santa Clara, CA, Oct 2016, pp. 456-469.