| Accelerating proximal policy optimization on cpu-fpga heterogeneous platforms Y Meng, S Kuppannagari, V Prasanna 2020 IEEE 28th Annual International Symposium on Field-Programmable Custom …, 2020 | 32 | 2020 |
| QTAccel: A generic FPGA based design for Q-table based reinforcement learning accelerators Y Meng, S Kuppannagari, R Rajat, A Srivastava, R Kannan, V Prasanna 2020 IEEE International Parallel and Distributed Processing Symposium …, 2020 | 19* | 2020 |
| DYNAMAP: Dynamic Algorithm Mapping Framework for Low Latency CNN Inference Y Meng, S Kuppannagari, R Kannan, V Prasanna The 2021 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays …, 2021 | 16 | 2021 |
| How to efficiently train your ai agent? characterizing and evaluating deep reinforcement learning on heterogeneous platforms Y Meng, Y Yang, S Kuppannagari, R Kannan, V Prasanna 2020 IEEE High Performance Extreme Computing Conference (HPEC), 1-7, 2020 | 7 | 2020 |
| A framework for mapping drl algorithms with prioritized replay buffer onto heterogeneous platforms C Zhang, Y Meng, V Prasanna IEEE Transactions on Parallel and Distributed Systems, 2023 | 6 | 2023 |
| How to avoid zero-spacing in fractionally-strided convolution? a hardware-algorithm co-design methodology Y Meng, S Kuppannagari, R Kannan, V Prasanna 2021 IEEE 28th International Conference on High Performance Computing, Data …, 2021 | 6 | 2021 |
| Ppoaccel: A high-throughput acceleration framework for proximal policy optimization Y Meng, S Kuppannagari, R Kannan, V Prasanna IEEE Transactions on Parallel and Distributed Systems 33 (9), 2066-2078, 2021 | 5 | 2021 |
| Accelerator design and exploration for deformable convolution networks Y Meng, H Men, V Prasanna 2022 IEEE Workshop on Signal Processing Systems (SiPS), 1-6, 2022 | 4* | 2022 |
| FPGA acceleration of deep reinforcement learning using on-chip replay management Y Meng, C Zhang, V Prasanna Proceedings of the 19th ACM International Conference on Computing Frontiers …, 2022 | 4 | 2022 |
| A framework for monte-carlo tree search on cpu-fpga heterogeneous platform via on-chip dynamic tree management Y Meng, R Kannan, V Prasanna Proceedings of the 2023 ACM/SIGDA International Symposium on Field …, 2023 | 3 | 2023 |
| Accelerating Monte-Carlo Tree Search on CPU-FPGA Heterogeneous Platform Y Meng, R Kannan, V Prasanna 2022 32nd International Conference on Field-Programmable Logic and …, 2022 | 3 | 2022 |
| A Software-Hardware Co-Optimized Toolkit for Deep Reinforcement Learning on Heterogeneous Platforms Y Meng, M Kinsner, D Singh, MA Iyer, V Prasanna arXiv preprint arXiv:2311.09445, 2023 | 1 | 2023 |
| Evaluating multi-agent reinforcement learning on heterogeneous platforms S Wiggins, Y Meng, R Kannan, V Prasanna Artificial Intelligence and Machine Learning for Multi-Domain Operations …, 2023 | 1 | 2023 |
| Accelerating Deep Neural Network guided MCTS using Adaptive Parallelism Y Meng, Q Wang, T Zu, V Prasanna Proceedings of the SC'23 Workshops of The International Conference on High …, 2023 | | 2023 |
| Accelerating Multi-Agent DDPG on CPU-FPGA Heterogeneous Platform S Wiggins, Y Meng, R Kannan, V Prasanna 2023 IEEE High Performance Extreme Computing Conference (HPEC), 1-7, 2023 | | 2023 |
| Characterizing Speed Performance of Multi-Agent Reinforcement Learning S Wiggins, Y Meng, R Kannan, V Prasanna arXiv preprint arXiv:2309.07108, 2023 | | 2023 |
| End to End Framework for CNN Acceleration on FPGAs with Dynamic Algorithm Mapping H Liu, Y Meng, SR Kuppannagari, VK Prasanna Proceedings of the 2022 Fourteenth International Conference on Contemporary …, 2022 | | 2022 |
| FGYM: Toolkit for Benchmarking FPGA based Reinforcement Learning Algorithms N Peura, Y Meng, S Kuppannagari, V Prasanna 2021 31st International Conference on Field-Programmable Logic and …, 2021 | | 2021 |
Viktor K. PrasannaUniversity of Southern California在 usc.edu 的电子邮件经过验证
