About me

I am a Ph.D student in Computer Engineering at University of Southern California under the advisement of professor Viktor K. Prasanna.

My research interest is accelerating computationally intensive algorithms on various computing platforms. I am currently working on accelerating Deep Reinforcement Learning algorithms on FPGA.

Contact me via email [ymeng643@usc.edu]

Education

Aug 2019 - Present:

University of Southern California
Ph.D. Student in Computer Engineering

Aug 2015 - May 2019:

Rensselaer Polytechnic Institute
Bachelor of Science, Electrical Engineering - Computer and Systems Engineering

Research & Publications

• How to Avoid Zero-Spacing in Fractionally-Strided Convolution? A Hardware-Algorithm Co-Design Methodology (First Author)
  • Accepted as a full paper in the 28th edition of the IEEE International Conference on High Performance Computing, Data, and Analytics. (HiPC 2021)

  • Code

    Paper

• FGYM: Toolkit for Benchmarking FPGA based Reinforcement Learning Algorithms (Second Author)
  • Accepted as a Demo Night Presentation in the 2021 Field-Programmable Logic and Applications (FPL 2021)

  • Code

    Abstract

• DYNAMAP: Dynamic Algorithm Mapping Framework for Low Latency CNN Inference (First Author)
  • Accepted as a full paper in the 2021 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA 2021)
• How to Efficiently Train Your AI Agent? Characterizing and Evaluating Deep Reinforcement Learning on Heterogeneous Platforms (First Author)
  • Accepted as a Outstanding Student Paper Award in the 2020 IEEE High Performance Extreme Computing Conference (HPEC 2020)
• PPOAccel: A High-Throughput Acceleration Framework for Proximal Policy Optimization (First Author)
  • Published in the journal proceeding of Transactions on Parallel and Distributed Systems (TPDS)
• Accelerating Proximal Policy Optimization on CPU-FPGA heterogeneous platforms (First Author)
  • Accepted as a full paper in the 28th IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM 2020)
• Accelerating Table based Q learning on FPGA (Co-First Author)
  • Accepted as a poster in the 28th ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA 2020)
• Mesh adaptation on FPGA
  • Advisor: Mark Shephard, Scientific Computation Research Center, Rensselaer Polytechinic Institute

Work Experience

• Graduate Teaching Assistant, University of Souuthern California
  • Parallel and Distributed Computing
  • Accelerated Computing using FPGAs
  • Parallel Programming
• Undergraduate Teaching Assistant, Rensselaer Polytecnic Institute
  • Foundations of Computer Science
  • Laboratory Introduction to Embedded Control

• Electronics Engineer Co-op, Hasbro. Inc

• Technology Counselor, Emagination Computer Camps

Links

My Github
My Linkedin