# Survey reviews over 100 studies on reinforcement learning for FPGA and IC optimization

Published: 2026-10-22 · Updated: 2026-10-22 · Category: Research

Source page: https://www.sipply.dev/story/survey-reviews-over-100-studies-on-reinforcement-learning-c8be7101 · 中文: https://www.sipply.dev/zh/story/survey-reviews-over-100-studies-on-reinforcement-learning-c8be7101

## Summary

A comprehensive survey has been published that examines more than 100 studies focused on the application of reinforcement learning (RL) in the realm of hardware acceleration and system-level optimization on FPGA and integrated circuit platforms. The survey details various architectures, strategies, and performance metrics that have been explored in this field. Additionally, it highlights the challenges faced by researchers and outlines potential future research directions that could enhance the effectiveness of RL in hardware applications.

## Key takeaways

- The survey covers over 100 studies on RL applications in hardware optimization.
- It addresses various architectures and performance metrics relevant to FPGA and IC platforms.
- Future research directions are suggested to overcome current challenges in the field.

## Why it matters

This survey consolidates existing knowledge and identifies gaps in the application of RL for hardware systems, paving the way for advancements in technology.

- **Product managers:** Product managers can leverage insights from this survey to inform product development strategies that incorporate RL for improved hardware performance.
- **Developers:** Developers and engineers can utilize the findings to enhance their understanding of RL applications, leading to more efficient designs in FPGA and IC systems.
- **Students and job seekers:** Students and job-seekers can gain valuable insights into a growing field, positioning themselves for future opportunities in hardware optimization and machine learning.

## Sources

- [Reinforcement learning for hardware acceleration and system-level optimization on FPGA and integrated-circuit platforms: A survey](https://arxiv.org/abs/2610.28699) (arXiv cs.LG, 2026-10-22)

## 中文

### 综述回顾了100多项关于FPGA和IC优化的强化学习研究

一项综合性综述已发布，审查了100多项集中于强化学习（RL）在FPGA和集成电路平台的硬件加速和系统级优化应用的研究。该综述详细介绍了该领域探索的各种架构、策略和性能指标。此外，它还强调了研究人员面临的挑战，并概述了可能增强RL在硬件应用中有效性的未来研究方向。

**要点：**

- 该综述涵盖了100多项关于硬件优化中RL应用的研究。
- 它涉及与FPGA和IC平台相关的各种架构和性能指标。
- 建议未来的研究方向以克服该领域当前的挑战。

**为什么重要：** 该综述整合了现有知识，识别了RL在硬件系统应用中的空白，为技术进步铺平了道路。

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