A4 Refereed article in a conference publication
Goal-Driven Autonomy for Efficient On-chip Resource Management: Transforming Objectives to Goals
Authors: Elham Shamsa, Anil Kanduri, Amir M. Rahmani, Pasi Liljeberg, Axel Jantsch, Nikil Dutt
Editors: Jürgen Teich, Franco Fummi
Conference name: Design, Automation & Test in Europe Conference & Exhibition
Publication year: 2019
Journal: Proceedings : Design, Automation, and Test in Europe Conference and Exhibition
Book title : Proceedings of the 2019 Design, Automation & Test in Europe (DATE)
Journal name in source: 2019 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE)
Journal acronym: DES AUT TEST EUROPE
Series title: Proceedings : Design, Automation, and Test in Europe Conference and Exhibition
First page : 1397
Last page: 1402
Number of pages: 6
ISBN: 978-3-9819263-2-3
ISSN: 1530-1591
DOI: https://doi.org/10.23919/DATE.2019.8715134
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/41241772
Run-time resource allocation of heterogeneous multi-core systems is challenging with varying workloads and limited power and energy budgets. User interaction within these systems changes the performance requirements, often conflicting with concurrent applications' objective and system constraints. Current resource allocation approaches focus on optimizing fixed objective, ignoring the variation in system and applications' objective at run-time. For an efficient resource allocation, the system has to operate autonomously by formulating a hierarchy of goals. We present goal-driven autonomy (GDA) for on-chip resource allocation decisions, which allows systems to generate and prioritize goals in response to the workload and system dynamic variation. We implemented a proof-of-concept resource management framework that integrates the proposed goal management control to meet power, performance and user requirements simultaneously. Experimental results on an Exynos platform containing ARM's big. LITTLE-based heterogeneous multi-processor (HMP) show the effectiveness of GDA in efficient resource allocation in comparison with existing fixed objective policies.
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