基于区域感知和共享路径经验的片上网络高效路由强化学习框架

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2023-08-25 DOI:10.1109/mdat.2023.3306719

Kamil Khan, Sudeep Pasricha

引用次数: 0

摘要

在本文中，作者介绍了一种基于区域拥塞感知强化学习(RL)的片上网络(NoC)架构路由策略。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A Reinforcement Learning Framework With Region-Awareness and Shared Path Experience for Efficient Routing in Networks-on-Chip

In this article, the authors introduce a regional congestion-aware reinforcement learning (RL)-based routing policy for Network-on-Chip (NoC) architectures.

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.