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Multi-objective local-search optimization using reliability importance measuring

Khosravi F, Reimann F, Glaß M, Teich J (2014)

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Publication Status: Published

Publication Type: Conference contribution, Conference Contribution

Publication year: 2014

Publisher: Institute of Electrical and Electronics Engineers Inc.

Pages Range: 1-6

Article Number: 2593164

Conference Proceedings Title: Proceedings of the 51st Design Automation Conference (DAC 2014)

Event location: San Francisco, CA

ISBN: 9781479930173

DOI: 10.1145/2593069.2593164

Abstract

In recent years, reliability has become a major issue and ob- jective during the design of embedded systems. Here, differ- ent techniques to increase reliability like hardware-/software- based redundancy or component hardening are applied sys- tematically during Design Space Exploration (DSE), aiming at achieving highest reliability at lowest possible cost. Exist- ing approaches typically solely provide reliability measures, e. g. failure rate or Mean-Time-To-Failure (MTTF), to the optimization engine, poorly guiding the search which parts of the implementation to change. As a remedy, this work proposes an efficient approach that (a) determines the im- portance of resources with respect to the system's reliability and (b) employs this knowledge as part of a local search to guide the optimization engine which components/design de- cisions to investigate. First, we propose a novel approach to derive Importance Measures (IMs) using a structural eval- uation of Success Trees (STs). Since ST-based reliability analysis is already used for MTTF calculation, our approach comes at almost no overhead. Second, we enrich the global DSE with a local search. Here, we propose strategies guided by the IMs that directly change and enhance the implemen- tation. In our experimental setup, the available measures to enhance reliability are the selection of hardening levels during resource allocation and software-based redundancy during task binding; exemplarily, the proposed local search considers the selected hardening levels. The results show that the proposed method outperforms a state-of-the-Art ap- proach regarding optimization quality, particularly in the search for highly-reliable yet affordable implementations { at negligible runtime overhead. Copyright 2014 ACM.

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How to cite

APA:

Khosravi, F., Reimann, F., Glaß, M., & Teich, J. (2014). Multi-objective local-search optimization using reliability importance measuring. In Proceedings of the 51st Design Automation Conference (DAC 2014) (pp. 1-6). San Francisco, CA, US: Institute of Electrical and Electronics Engineers Inc..

MLA:

Khosravi, Faramarz, et al. "Multi-objective local-search optimization using reliability importance measuring." Proceedings of the 51st Annual Design Automation Conference, DAC 2014, San Francisco, CA Institute of Electrical and Electronics Engineers Inc., 2014. 1-6.

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