Reinforcement Learning based Interconnection Routing for Adaptive Traffic Optimization
This work addresses performance optimization in computer architectures, specifically for NoC routing, but appears incremental as it applies existing RL methods to a known problem.
The paper tackled optimizing runtime performance in Network-on-Chip (NoC) by applying reinforcement learning to learn optimal routing algorithms, achieving near-optimal solutions across different environment states.
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