Accelerating Proximal Policy Optimization on CPU-FPGA Heterogeneous Platforms. - Yuan Meng 001; Sanmukh R. Kuppannagari; Viktor K. Prasanna (FCCM, 2020)

QTAccel: A Generic FPGA based Design for Q-Table based Reinforcement Learning Accelerators. - Rachit Rajat; Yuan Meng 001; Sanmukh R. Kuppannagari; Ajitesh Srivastava; Viktor K. Prasanna; Rajgopal Kannan (FPGA, 2020)

How to Efficiently Train Your AI Agent? Characterizing and Evaluating Deep Reinforcement Learning on Heterogeneous Platforms. - Yuan Meng 001; Yang Yang 111; Sanmukh R. Kuppannagari; Rajgopal Kannan; Viktor K. Prasanna (HPEC, 2020)

DYNAMAP: Dynamic Algorithm Mapping Framework for Low Latency CNN Inference. - Yuan Meng 001; Sanmukh R. Kuppannagari; Rajgopal Kannan; Viktor K. Prasanna (FPGA, 2021)

FGYM: Toolkit for Benchmarking FPGA based Reinforcement Learning Algorithms. - Nathaniel Peura; Yuan Meng 001; Sanmukh R. Kuppannagari; Viktor K. Prasanna (FPL, 2021)

How to Avoid Zero-Spacing in Fractionally-Strided Convolution? A Hardware-Algorithm Co-Design Methodology. - Yuan Meng 001; Sanmukh R. Kuppannagari; Rajgopal Kannan; Viktor K. Prasanna (HiPC, 2021)

FPGA acceleration of deep reinforcement learning using on-chip replay management. - Yuan Meng 001; Chi Zhang 022; Viktor K. Prasanna (CF, 2022)

Accelerating Deformable Convolution Networks. - Yuan Meng 001; Hongjiang Men; Viktor K. Prasanna (FCCM, 2022)

Accelerating Monte-Carlo Tree Search on CPU-FPGA Heterogeneous Platform. - Yuan Meng 001; Rajgopal Kannan; Viktor K. Prasanna (FPL, 2022)

End to End Framework for CNN Acceleration on FPGAs with Dynamic Algorithm Mapping. - Haomei Liu; Yuan Meng 001; Sanmukh Rao Kuppannagari; Viktor K. Prasanna (IC3, 2022)

Accelerator Design and Exploration for Deformable Convolution Networks. - Yuan Meng 001; Hongjiang Men; Viktor K. Prasanna (SiPS, 2022)

PPOAccel: A High-Throughput Acceleration Framework for Proximal Policy Optimization. - Yuan Meng 001; Sanmukh R. Kuppannagari; Rajgopal Kannan; Viktor K. Prasanna (IEEE Trans. Parallel Distributed Syst., 2022)

Characterizing Speed Performance of Multi-Agent Reinforcement Learning. - Samuel Wiggins; Yuan Meng 001; Rajgopal Kannan; Viktor K. Prasanna (DATA, 2023)

A Framework for Monte-Carlo Tree Search on CPU-FPGA Heterogeneous Platform via on-chip Dynamic Tree Management. - Yuan Meng 001; Rajgopal Kannan; Viktor K. Prasanna (FPGA, 2023)

Accelerating Multi-Agent DDPG on CPU-FPGA Heterogeneous Platform. - Samuel Wiggins; Yuan Meng 001; Rajgopal Kannan; Viktor K. Prasanna (HPEC, 2023)

Accelerating Deep Neural Network guided MCTS using Adaptive Parallelism. - Yuan Meng 001; Qian Wang 041; Tianxin Zu; Viktor K. Prasanna (SC Workshops, 2023)

A Software-Hardware Co-Optimized Toolkit for Deep Reinforcement Learning on Heterogeneous Platforms. - Yuan Meng 001; Michael Kinsner; Deshanand P. Singh; Mahesh A. Iyer; Viktor K. Prasanna (CoRR, 2023)

A Framework for Mapping DRL Algorithms With Prioritized Replay Buffer Onto Heterogeneous Platforms. - Chi Zhang 022; Yuan Meng 001; Viktor K. Prasanna (IEEE Trans. Parallel Distributed Syst., 2023)

PEARL: Enabling Portable, Productive, and High-Performance Deep Reinforcement Learning using Heterogeneous Platforms. - Yuan Meng 001; Michael Kinsner; Deshanand P. Singh; Mahesh A. Iyer; Viktor K. Prasanna (CF, 2024)

A Heterogeneous Acceleration System for Attention-Based Multi-Agent Reinforcement Learning. - Samuel Wiggins; Yuan Meng 001; Mahesh A. Iyer; Viktor K. Prasanna (FPL, 2024)

An Acceleration Framework for Deep Reinforcement Learning Using Heterogeneous Systems. - Yuan Meng 001; Mahesh A. Iyer; Viktor K. Prasanna (IEEE Trans. Parallel Distributed Syst., 2025)