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Selective abstraction and stochastic methods for scalable power modelling of heterogeneous systems

Lookup NU author(s): Dr Ashur Rafiev, Dr Fei Xia, Dr Alexei Iliasov, Rem Gensh, Ali Aalsaud, Professor Alexander Romanovsky, Professor Alex Yakovlev

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This is the final published version of a conference proceedings (inc. abstract) that has been published in its final definitive form by IEEE Computer Society, 2017.

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Abstract

© 2016 ECSI. With the increase of system complexity in both platforms and applications, power modelling of heterogeneous systems is facing grand challenges from the model scalability issue. To address these challenges, this paper studies two systematic methods: Selective abstraction and stochastic techniques. The concept of selective abstraction via black-boxing is realised using hierarchical modelling and cross-layer cuts, respecting the concepts of boxability and error contamination. The stochastic aspect is formally underpinned by Stochastic Activity Networks (SANs). The proposed method is validated with experimental results from Odroid XU3 heterogeneous 8-core platform and is demonstrated to maintain high accuracy while improving scalability.


Publication metadata

Author(s): Rafiev A, Xia F, Iliasov A, Gensh R, Aalsaud A, Romanovsky A, Yakovlev A

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Forum on Specification and Design Languages (FDL)

Year of Conference: 2017

Online publication date: 20/03/2017

Acceptance date: 02/04/2016

Date deposited: 30/05/2017

Publisher: IEEE Computer Society

URL: https://doi.org/10.1109/FDL.2016.7880376

DOI: 10.1109/FDL.2016.7880376

Data Source Location: http://dx.doi.org/10.17634/123238-3

Library holdings: Search Newcastle University Library for this item

ISBN: 9791092279177


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