Papers › Reinforcement Learning based Interconnection Routing for Adaptive Traffic Optimization

Reinforcement Learning based Interconnection Routing for Adaptive Traffic Optimization

13 Aug 2019arXiv:1908.04484archive 2025-07-28

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

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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BIG-bench Machine Learning Reinforcement Learning Reinforcement Learning (RL)reinforcement-learning

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