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)
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- 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)
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- 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
Links
My Github
My Linkedin