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

Learning-based run-time power and energy management of multi/many-core systems: current and future trends
Learning-based run-time power and energy management of multi/many-core systems: current and future trends
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 ever-increasing 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.
Multi/many-core systems, power/energy optimization, run-time, machine learning
Singh, Amit
bb67d43e-34d9-4b58-9295-8b5458270408
Leech, Charles
6ba70c54-3792-41cd-a8d6-9e8884ae004f
Basireddy, Karunakar Reddy
5bfb0b2e-8242-499a-a52b-e813d9a90889
Al-Hashimi, Bashir
0b29c671-a6d2-459c-af68-c4614dce3b5d
Merrett, Geoffrey
89b3a696-41de-44c3-89aa-b0aa29f54020
Singh, Amit
bb67d43e-34d9-4b58-9295-8b5458270408
Leech, Charles
6ba70c54-3792-41cd-a8d6-9e8884ae004f
Basireddy, Karunakar Reddy
5bfb0b2e-8242-499a-a52b-e813d9a90889
Al-Hashimi, Bashir
0b29c671-a6d2-459c-af68-c4614dce3b5d
Merrett, Geoffrey
89b3a696-41de-44c3-89aa-b0aa29f54020

Singh, Amit, Leech, Charles, Basireddy, Karunakar Reddy, Al-Hashimi, Bashir and Merrett, Geoffrey (2017) Learning-based run-time power and energy management of multi/many-core systems: current and future trends. Journal of Low Power Electronics.

Record type: Article

Abstract

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 ever-increasing 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.

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Accepted/In Press date: 4 June 2017
e-pub ahead of print date: 1 September 2017
Keywords: Multi/many-core systems, power/energy optimization, run-time, machine learning

Identifiers

Local EPrints ID: 412826
URI: https://eprints.soton.ac.uk/id/eprint/412826
PURE UUID: 5436e561-fb83-4c22-855b-cbc7a619f40f
ORCID for Charles Leech: ORCID iD orcid.org/0000-0002-2403-3873
ORCID for Karunakar Reddy Basireddy: ORCID iD orcid.org/0000-0001-9755-1041
ORCID for Geoffrey Merrett: ORCID iD orcid.org/0000-0003-4980-3894

Catalogue record

Date deposited: 02 Aug 2017 16:30
Last modified: 06 Jun 2018 12:42

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Contributors

Author: Amit Singh
Author: Charles Leech ORCID iD
Author: Karunakar Reddy Basireddy ORCID iD
Author: Geoffrey Merrett ORCID iD

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