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

Zheqi Yu, Pedro Machado, Adnan Zahid, Amir M. Abdulghani, Kia Dashtipour, Hadi Heidari, Muhammad A. Imran, Qammer H. Abbasi

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
4 Downloads (Pure)

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 a Xilinx 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.

Original languageEnglish
Article number1812
JournalElectronics
Volume9
Issue number11
DOIs
Publication statusPublished - 2 Nov 2020

Keywords

  • Heterogeneous computing
  • Machine learning
  • Power and performance optimisation
  • Reinforcement learning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Hardware and Architecture
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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