Research Repository

Learning-based run-time power and energy management of multi/many-core systems: current and future trends

Singh, Amit Kumar and Leech, Charles and Basireddy, Karunakar Reddy and Al-Hashimi, Bashir and Merrett, Geoff V (2017) 'Learning-based run-time power and energy management of multi/many-core systems: current and future trends.' Journal of Low Power Electronics, 13 (3). pp. 310-325. ISSN 1546-1998

jolpe_final.pdf - Accepted Version

Download (948kB) | Preview


Multi/Many-core systems are prevalent in several application domains targeting different scales of computing such as embedded and cloud computing. These systems are able to fulfil the everincreasing performance requirements by exploiting their parallel processing capabilities. However, effective power/energy management is required during system operations due to several reasons such as to increase the operational time of battery operated systems, reduce the energy cost of datacenters, and improve thermal efficiency and reliability. This article provides an extensive survey of learning-based run-time power/energy management approaches. The survey includes a taxonomy of the learning-based approaches. These approaches perform design-time and/or run-time power/energy management by employing some learning principles such as reinforcement learning. The survey also highlights the trends followed by the learning-based run-time power management approaches, their upcoming trends and open research challenges.

Item Type: Article
Uncontrolled Keywords: Multi/many-core systems, power/energy optimization, run-time, machine learning
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 24 Sep 2018 14:30
Last Modified: 23 Sep 2022 19:21

Actions (login required)

View Item View Item