arXiv cs.AI· Hamza Kheddar·· 13 小时前AI 评分18
FPGA 与集成电路平台上的强化学习硬件加速与系统级优化综述
Reinforcement learning for hardware acceleration and system-level optimization on FPGA and integrated-circuit platforms: A survey
AI 导读
一篇综述系统梳理了 100 余项在 FPGA 与 IC 平台上实现强化学习的研究，按 RL 家族（基于模型的 MDP、值方法、深度 RL、actor-critic、策略梯度、多智能体）分类，并从抽象层级、硬件实现策略和应用领域三个维度进行归类。该综述统一分析了加速、可重构架构、能耗感知控制与系统级优化，比较了资源利用率、延迟、吞吐、功耗和性能增益，并总结了反复出现的架构模式、实际限制与开放挑战。
正文 · 原文
Abstract:Reinforcement learning (RL) enables adaptive and autonomous decision-making in embedded, edge, and heterogeneous computing systems. Field-programmable gate arrays (FPGAs) and integrated circuits (ICs) offer low latency, energy efficiency, and architectural flexibility for RL deployment, while introducing challenges in resource constraints, implementation complexity, and hardware-algorithm co-design. This review surveys more than 100 studies on RL implementations across FPGA and IC platforms, emphasizing architectures, implementation strategies, performance evaluation, and applications. The literature is organized by RL family, including model-based Markov decision processes, classical value-based methods, deep RL, actor-critic, policy-gradient, and multi-agent approaches. We further classify studies by abstraction level, hardware realization strategy, and application domain, examining how RL algorithms map onto reconfigurable and fixed hardware. Unlike surveys restricted to particular algorithms or platforms, this work provides a unified analysis of FPGA and IC implementations, covering acceleration, reconfigurable architectures, energy-aware control, and system-level optimization. Comparative assessments consider resource utilization, latency, throughput, power consumption, and reported performance gains. Finally, we identify recurring architectural patterns, practical limitations, open challenges, and research directions for efficient RL-driven hardware systems. The survey offers a consolidated reference for researchers developing and evaluating RL solutions on specialized hardware.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Performance (cs.PF); Robotics (cs.RO); Systems and Control (eess.SY) |
| Cite as: | arXiv:2610.28699 [cs.LG] |
| (or arXiv:2610.28699v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.28699 arXiv-issued DOI via DataCite (pending registration) |
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| Journal reference: | Journal of Systems Architecture, 2026 |
| Related DOI: | https://doi.org/10.1016/j.sysarc.2026.103874
DOI(s) linking to related resources |
Submission history
From: Hamza Kheddar [view email]
[v1]
Tue, 20 Oct 2026 11:15:34 UTC (1,201 KB)
来源：arXiv cs.AI · arxiv.org