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综述回顾了100多项关于FPGA和IC优化的强化学习研究Survey reviews over 100 studies on reinforcement learning for FPGA and IC optimization
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以下内容为 AI 自动翻译，英文原文附后供参考。
摘要
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。
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.
要点
该综述涵盖了100多项关于硬件优化中RL应用的研究。
The survey covers over 100 studies on RL applications in hardware optimization.
它涉及与FPGA和IC平台相关的各种架构和性能指标。
It addresses various architectures and performance metrics relevant to FPGA and IC platforms.
建议未来的研究方向以克服该领域当前的挑战。
Future research directions are suggested to overcome current challenges in the field.
为何重要
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。
This survey consolidates existing knowledge and identifies gaps in the application of RL for hardware systems, paving the way for advancements in technology.
产品经理
产品经理可以利用该综述中的见解来指导产品开发策略，结合RL以改善硬件性能。
Product managers can leverage insights from this survey to inform product development strategies that incorporate RL for improved hardware performance.
开发者
开发人员和工程师可以利用这些发现来增强对RL应用的理解，从而在FPGA和IC系统中实现更高效的设计。
Developers and engineers can utilize the findings to enhance their understanding of RL applications, leading to more efficient designs in FPGA and IC systems.
学生与求职者
学生和求职者可以获得对一个不断增长领域的宝贵见解，为未来在硬件优化和机器学习方面的机会做好准备。
Students and job-seekers can gain valuable insights into a growing field, positioning themselves for future opportunities in hardware optimization and machine learning.
覆盖来源
来源标题与摘录保留原文，以便核对与追溯。
arXiv cs.LG
Reinforcement learning for hardware acceleration and system-level optimization on FPGA and integrated-circuit platforms: A surveyThe article surveys over 100 studies on the implementation of reinforcement learning (RL) in FPGA and integrated circuit (IC) platforms, detailing architectures, strategies, and performance metrics while addressing challenges and future research directions.