Reinforcement Learning based Interconnection Routing for Adaptive Traffic Optimization

2019-08-13 · 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

📄 PDF Abstract BibTeX arXiv:1908.04484

Code (2)

huckiyang/interconnect-routing-gym 공식 구현

felix0901/interconnect-routing-gym

Tasks

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

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