Reinforcement Learning based Interconnection Routing for Adaptive Traffic Optimization

Sheng-Chun Kao, Chao-Han Huck Yang, Pin-Yu Chen, Xiaoli Ma, Tushar Krishna
8/13/2019
cs.NIcs.AIcs.AR

Official code: felix0901/interconnect-routing-gym (17 stars, last commit 2022-05-12).

Abstract

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

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Code Implementations(1)

felix0901/interconnect-routing-gymOfficial100%

openai-gym style RL benchmark for interconnection network congestion control study

174Shell, PythonJul 15, 20194 years ago

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Cite this paper

@article{kao2019reinforcement,
  title  = {Reinforcement Learning based Interconnection Routing for Adaptive Traffic Optimization},
  author = {Sheng-Chun Kao and Chao-Han Huck Yang and Pin-Yu Chen and Xiaoli Ma and Tushar Krishna},
  year   = {2019},
  eprint = {1908.04484},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1908.04484}
}

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