Reinforcement learning based interconnection routing for adaptive traffic optimization
Reinforcement learning based interconnection routing for adaptive traffic optimization
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基于强化学习的互连路由，用于自适应流量优化
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
T. Krishna
中科院分区：
文献类型：
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作者：
Sheng;Chao;Pin;Xiaoli Ma;T. Krishna
将机器学习技术应用于计算机体系结构的设计和优化是一个很有前途的研究方向。优化片上网络(NoC)的运行时性能需要一个持续的学习框架。在这项工作中，我们展示了应用强化学习(RL)来优化NoC运行时性能的前景。我们提出了三种基于RL学习最优路由算法的方法。实验结果表明，该算法能够在不同的环境状态下成功地学习到一个近似最优解。
Applying Machine Learning (ML) techniques to design and optimize computer architectures is a promising research direction. Optimizing the runtime performance of a Network-on-Chip (NoC) necessitates a continuous learning framework. In this work, we demonstrate the promise of applying reinforcement learning (RL) to optimize NoC runtime performance. We present three RL-based methods for learning optimal routing algorithms. The experimental results show the algorithms can successfully learn a near-optimal solution across different environment states.