FPGA acceleration of deep reinforcement learning using on-chip replay management-Reference-Cited by-同舟云学术

FPGA acceleration of deep reinforcement learning using on-chip replay management

Published:2022-05-17 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 19th ACM International Conference on Computing Frontiers
language:
Short-container-title:

Author:

Meng Yuan¹,Zhang Chi¹,Prasanna Viktor¹

Affiliation:

1. University of Southern California

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3528416.3530227

Reference41 articles.

1. Greg Brockman Vicki Cheung Ludwig Pettersson Jonas Schneider John Schulman Jie Tang and Wojciech Zaremba. 2016. OpenAI Gym. arXiv:arXiv:1606.01540 Greg Brockman Vicki Cheung Ludwig Pettersson Jonas Schneider John Schulman Jie Tang and Wojciech Zaremba. 2016. OpenAI Gym. arXiv:arXiv:1606.01540

2. Black-box data-efficient policy search for robotics

3. FA3C

4. BlastFunction: A Full-stack Framework Bringing FPGA Hardware Acceleration to Cloud-native Applications

5. Utilizing cloud FPGAs towards the open neural network standard

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. PEARL: Enabling Portable, Productive, and High-Performance Deep Reinforcement Learning using Heterogeneous Platforms;Proceedings of the 21st ACM International Conference on Computing Frontiers;2024-05-07

2. FPGA-Accelerated Sim-to-Real Control Policy Learning for Robotic Arms;IEEE Transactions on Circuits and Systems II: Express Briefs;2024-03

3. DQN Algorithm Design for Fast Efficient Shortest Path System;2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC);2023-10-31

4. A Framework for Mapping DRL Algorithms With Prioritized Replay Buffer Onto Heterogeneous Platforms;IEEE Transactions on Parallel and Distributed Systems;2023-06