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Sanmukh R. Kuppannagari
Publications
- 2022
- [j3]Yuan Meng, Sanmukh R. Kuppannagari, Rajgopal Kannan, Viktor K. Prasanna:
PPOAccel: A High-Throughput Acceleration Framework for Proximal Policy Optimization. IEEE Trans. Parallel Distributed Syst. 33(9): 2066-2078 (2022) - [c37]Haomei Liu, Yuan Meng, Sanmukh Rao Kuppannagari, Viktor K. Prasanna:
End to End Framework for CNN Acceleration on FPGAs with Dynamic Algorithm Mapping. IC3 2022: 696-700 - 2021
- [c33]Yuan Meng, Sanmukh R. Kuppannagari, Rajgopal Kannan, Viktor K. Prasanna:
DYNAMAP: Dynamic Algorithm Mapping Framework for Low Latency CNN Inference. FPGA 2021: 183-193 - [c31]Nathaniel Peura, Yuan Meng, Sanmukh R. Kuppannagari, Viktor K. Prasanna:
FGYM: Toolkit for Benchmarking FPGA based Reinforcement Learning Algorithms. FPL 2021: 404 - [c29]Yuan Meng, Sanmukh R. Kuppannagari, Rajgopal Kannan, Viktor K. Prasanna:
How to Avoid Zero-Spacing in Fractionally-Strided Convolution? A Hardware-Algorithm Co-Design Methodology. HiPC 2021: 81-90 - 2020
- [c24]Yuan Meng, Sanmukh R. Kuppannagari, Viktor K. Prasanna:
Accelerating Proximal Policy Optimization on CPU-FPGA Heterogeneous Platforms. FCCM 2020: 19-27 - [c23]Rachit Rajat, Yuan Meng, Sanmukh R. Kuppannagari, Ajitesh Srivastava, Viktor K. Prasanna, Rajgopal Kannan:
QTAccel: A Generic FPGA based Design for Q-Table based Reinforcement Learning Accelerators. FPGA 2020: 323 - [c22]Yuan Meng, Yang Yang, Sanmukh R. Kuppannagari, Rajgopal Kannan, Viktor K. Prasanna:
How to Efficiently Train Your AI Agent? Characterizing and Evaluating Deep Reinforcement Learning on Heterogeneous Platforms. HPEC 2020: 1-7 - [c19]Yuan Meng, Sanmukh R. Kuppannagari, Rachit Rajat, Ajitesh Srivastava, Rajgopal Kannan, Viktor K. Prasanna:
QTAccel: A Generic FPGA based Design for Q-Table based Reinforcement Learning Accelerators. IPDPS Workshops 2020: 107-114 - [i3]Yuan Meng, Sanmukh R. Kuppannagari, Rajgopal Kannan, Viktor K. Prasanna:
DYNAMAP: Dynamic Algorithm Mapping Framework for Low Latency CNN Inference. CoRR abs/2012.00912 (2020)
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