Energy and Performance Trade-Off Optimization in Heterogeneous Computing via Reinforcement Learning

Energy and Performance Trade-Off Optimization in Heterogeneous Computing via Reinforcement Learning

This paper suggests an optimisation approach in heterogeneous computing systems to balance energy power consumption and efficiency. The work proposes a power measurement utility for a reinforcement learning (PMU-RL) algorithm to dynamically adjust the resource utilisation of heterogeneous platforms...

Full description

Bibliographic Details
Main Authors: Zheqi Yu, Pedro Machado, Adnan Zahid, Amir M. Abdulghani, Kia Dashtipour, Hadi Heidari, Muhammad A. Imran, Qammer H. Abbasi
Format: Article
Language:English
Published: MDPI AG 2020-11-01
Series:Electronics
Subjects:
machine learning
power and performance optimisation
reinforcement learning
heterogeneous computing
Online Access:https://www.mdpi.com/2079-9292/9/11/1812

Internet

https://www.mdpi.com/2079-9292/9/11/1812

Similar Items