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...

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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

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