Energy and performance trade-off optimization in heterogeneous computing via reinforcement learning

Yu, Z, Machado, P ORCID: https://orcid.org/0000-0003-1760-3871, Zahid, A, Abdulghani, AM, Dashtipour, K, Heidari, H, Imran, MA and Abbasi, QH, 2020. Energy and performance trade-off optimization in heterogeneous computing via reinforcement learning. Electronics, 9 (11): 1812. ISSN 2079-9292

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Abstract

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 in order to minimise power consumption. A reinforcement learning(RL) technique is applied to analyse and optimise the resource utilisation of field programmable gate array (FPGA) control state capabilities, which is built for a simulation environment with aXilinx ZYNQ multi-processor systems-on-chip (MPSoC) board. In this study, the balance operation mode for improving power consumption and performance is established to dynamically change the programmable logic (PL) end work state. It is based on an RL algorithm that can quickly discover the optimization effect of PL on different workloads to improve energy efficiency. The results demonstrate a substantial reduction of 18% in energy consumption without affecting the application’s performance. Thus, the proposed PMU-RL technique has the potential to be considered for other heterogeneous computing platforms.

Item Type:	Journal article
Publication Title:	Electronics
Creators:	Yu, Z., Machado, P., Zahid, A., Abdulghani, A.M., Dashtipour, K., Heidari, H., Imran, M.A. and Abbasi, Q.H.
Publisher:	MDPI AG
Date:	2020
Volume:	9
Number:	11
ISSN:	2079-9292
Identifiers:	Number Type 10.3390/electronics9111812 DOI 1384437 Other
Rights:	© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	15 Jan 2021 14:31
Last Modified:	31 May 2021 15:07
URI:	https://irep.ntu.ac.uk/id/eprint/42045

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