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 (2016) Machine Learning for Run-Time Energy Optimisation in Many-Core Systems At Conference on Design, Automation and Test in Europe 2017 (DATE'17), Lausanne, Switzerland. 27 - 31 Mar 2017. 5 pp.

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Description/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.

Item Type: Conference or Workshop Item (Other)
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
ePrint ID: 404512
Date :
Date Event
10 November 2016Accepted/In Press
27 March 2017Published
Date Deposited: 10 Jan 2017 11:35
Last Modified: 17 Apr 2017 00:36
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/404512

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