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Silent Data Corruption Estimation and Mitigation Without Fault Injection [Estimation et atténuation de la corruption silencieuse des données sans injection de faute]
Islamic Azad University, Borujerd, Iran.
Halmstad University, School of Information Technology. Islamic Azad University, Borujerd, Iran.ORCID iD: 0000-0002-2874-6256
Islamic Azad University, Borujerd, Iran; Kharazmi University, Tehran, Iran.ORCID iD: 0000-0003-1506-3533
2022 (English)In: IEEE Canadian Journal of Electrical and Computer Engineering, E-ISSN 2694-1783, Vol. 45, no 3, p. 318-327Article in journal (Refereed) Published
Abstract [en]

Silent data corruptions (SDCs) have been always regarded as the serious effect of radiation-induced faults. Traditional solutions based on redundancies are very expensive in terms of chip area, energy consumption, and performance. Consequently, providing low-cost and efficient approaches to cope with SDCs has received researchers’ attention more than ever. On the other hand, identifying SDC-prone data and instruction in a program is a very challenging issue, as it requires time-consuming fault injection processes into different parts of a program. In this article, we present a cost-efficient approach to detecting and mitigating the rate of SDCs in the whole program with the presence of multibit faults without a fault injection process. This approach uses a combination of machine learning and a metaheuristic algorithm that predicts the SDC event rate of each instruction. The evaluation results show that the proposed approach provides a high level of detection accuracy of 99% while offering a low-performance overhead of 58%. © 2022 IEEE.

Place, publisher, year, edition, pages
Dundas, ON: IEEE, 2022. Vol. 45, no 3, p. 318-327
Keywords [en]
Duplication, multibit fault, reliability, silent data corruption (SDC), single-bit fault
National Category
Computer Engineering
Identifiers
URN: urn:nbn:se:hh:diva-51377DOI: 10.1109/ICJECE.2022.3189043ISI: 000852245400001Scopus ID: 2-s2.0-85166390630OAI: oai:DiVA.org:hh-51377DiVA, id: diva2:1787552
Available from: 2023-08-14 Created: 2023-08-14 Last updated: 2023-08-14Bibliographically approved

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Fazeli, Mahdi

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