Can I read Reinforcement Learning Based Interconnection Routing for Adaptive Traffic Optimization on EtoBox?

Reinforcement Learning Based Interconnection Routing for Adaptive Traffic Optimization by Sheng-Chun Kao*1, Chao-Han Huck Yang*1, Pin-Yu Chen2, Xiaoli Ma1, Tushar Krishna1 is a scholarly article available to read on EtoBox.

What is Reinforcement Learning Based Interconnection Routing for Adaptive Traffic Optimization about?

Applying Machine Learning (ML) techniques to design and optimize computer architectures is a promising research direction. Optimizing the runtime performance of a Networkon-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.

Author

Sheng-Chun Kao*1, Chao-Han Huck Yang*1, Pin-Yu Chen2, Xiaoli Ma1, Tushar Krishna1

Publisher

ACM

Published

2019

Language

EN