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Architecture optimization: Speed or accuracy? Both!
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-0401-1036
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-8486-5110
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. IS (Embedded Systems).ORCID iD: 0000-0002-8461-0230
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-5278-755X
2018 (English)In: Software quality journal, ISSN 0963-9314, E-ISSN 1573-1367, Vol. 26, no 2, p. 661-684Article in journal (Refereed) Published
Abstract [en]

Embedded systems are becoming more and more complex, thus demanding innovative means to tame their challenging development. Among others, early architecture optimization represents a crucial activity in the development of embedded systems to maximise the usage of their limited resources and to respect their real-time requirements. Typically, architecture optimization seeks good architecture candidates based on model-based analysis. Leveraging abstractions and estimates, this analysis usually produces approximations useful for comparing architecture candidates. Nonetheless, approximations do not provide enough accuracy in estimating crucial extra-functional properties. In this article, we provide an architecture optimization framework that profits from both the speed of model-based predictions and the accuracy of execution-based measurements. Model-based optimization rapidly finds a good architecture candidate, which is refined through optimization based on monitored executions of automatically generated code. Moreover, the framework enables the developer to leverage her optimization experience. More specifically, the developer can use runtime monitoring of generated code execution to manually adjust task allocation at modelling level, and commit the changes without halting execution. In the article, our architecture optimization mechanism is first described from a general point of view and then exploited for optimizing the allocation of software tasks to the processing cores of a multicore embedded system; we target extra-functional properties that can be concretely represented and automatically compared for different architectural alternatives (such as memory consumption, energy consumption, or responsetime).

Place, publisher, year, edition, pages
2018. Vol. 26, no 2, p. 661-684
Keywords [en]
architecture optimization, software quality, model-driven engineering, model transformations, back-propagation, execution, monitoring, multicore, embedded systems
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-33817DOI: 10.1007/s11219-016-9343-5ISI: 000433521200017Scopus ID: 2-s2.0-84995449880OAI: oai:DiVA.org:mdh-33817DiVA, id: diva2:1048579
Projects
RALF3 - Software for Embedded High Performance ArchitecturesSMARTCore: Model-driven support for deployment optimization of embedded systems on multicoreORION - Decision Support for Component-Based Software Engineering of Cyber-Physical SystemsAvailable from: 2016-11-21 Created: 2016-11-21 Last updated: 2018-06-14Bibliographically approved

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Ciccozzi, FedericoFeljan, JurajCarlson, JanCrnkovic, Ivica
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