Reinforcement Learning based Interconnection Routing for Adaptive Traffic Optimization

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

doi:10.1145/3313231.335236

Reinforcement Learning based Interconnection Routing for Adaptive Traffic Optimization

Networking and Internet Architecture 2019-08-14 v1 Artificial Intelligence Hardware Architecture Machine Learning Systems and Control Systems and Control

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

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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

Keywords

reinforcement learning traffic signal control multi-view learning

Cite

@article{arxiv.1908.04484,
  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},
  journal= {arXiv preprint arXiv:1908.04484},
  year   = {2019}
}

Reinforcement Learning based Interconnection Routing for Adaptive Traffic Optimization

Abstract

Keywords

Cite

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