arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1908.04484cs.NIcs.AIcs.ARcs.LGcs.SYeess.SY

Reinforcement Learning based Interconnection Routing for Adaptive Traffic Optimization

Georgia Institute of Technology(佐治亚理工学院);IBM Watson AI Foundation Group(IBM沃森人工智能基金会组)

Sheng-Chun Kao, Chao-Han Huck Yang, Pin-Yu Chen, Xiaoli Ma, Tushar Krishna

英文摘要:

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. Reproducible Code: github.com/huckiyang/interconnect-routing-gym

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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