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Machine Learning for Run-Time Energy Optimisation in Many-Core Systems

Biswas, Dwaipayan, Balagopal, Vibishna, Shafik, Rishad, Al-Hashimi, Bashir B M and Merrett, Geoff V (2017) Machine Learning for Run-Time Energy Optimisation in Many-Core Systems In 2017 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE. 5 pp.

Record type: Conference or Workshop Item (Paper)

Abstract

In recent years, the focus of computing has moved away from performance-centric serial computation to energy-efficient parallel computation. This necessitates run-time optimisation techniques to address the dynamic resource requirements of different applications on many-core architectures. In this paper, we report on intelligent run-time algorithms which have been experimentally validated for managing energy and application performance in many-core embedded system. The algorithms are underpinned by a cross-layer system approach where the hardware, system software and application layers work together to optimise the energy-performance trade-off. Algorithm development is motivated by the biological process of how a human brain (acting as an agent) interacts with the external environment (system) changing their respective states over time. This leads to a pay-off for the action taken, and the agent eventually learns to take the optimal/best decisions in future. In particular, our online approach uses a model-free reinforcement learning algorithm that suitably selects the appropriate voltage-frequency scaling based on workload prediction to meet the applications’ performance requirements and achieve energy savings of up to 16% in comparison to state-of-the-art-techniques, when tested on four ARM A15 cores of an ODROID-XU3 platform.

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More information

Accepted/In Press date: 10 November 2016
e-pub ahead of print date: 15 May 2017
Published date: 2017
Venue - Dates: Conference on Design, Automation and Test in Europe 2017 (DATE'17), Lausanne, Switzerland, 2017-03-27 - 2017-03-31
Organisations: Electronic & Software Systems

Identifiers

Local EPrints ID: 404512
URI: http://eprints.soton.ac.uk/id/eprint/404512
PURE UUID: 1d77d759-ef22-45a9-957b-a5e9efc0f016
ORCID for Geoff V Merrett: ORCID iD orcid.org/0000-0003-4980-3894

Catalogue record

Date deposited: 10 Jan 2017 11:35
Last modified: 17 Aug 2017 16:30

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Contributors

Author: Dwaipayan Biswas
Author: Vibishna Balagopal
Author: Rishad Shafik
Author: Geoff V Merrett ORCID iD

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