Revisiting software-based soft error mitigation techniques via accurate error generation and propagation models

Abstract : Radiation-induced soft errors are growing reliability concerns, especially in mission- and safety-critical systems. A variety of software-based fault tolerant techniques have widely been proposed and used to mitigate soft errors at the application-level. Such techniques are typically evaluated using statistical fault injection at software-visible variables of the system as fault injection at higher levels of abstraction is much faster than logic-level or Register Transfer Level (RTL). Recent studies revealed that software-based fault injection techniques are not accurate for analyzing soft errors originating in flip-flops. However, the effectiveness of such techniques for evaluation of the entire processor including register-files and cache arrays are not studied yet. In this paper, we comprehensively study the soft error rate of several workloads and their protected version using software-based fault tolerance by performing detailed error generation and propagation analysis at hardware-level. Our detailed experimental analysis shows that there is no significant correlation between the results of hardware- and software-based fault injection for the effectiveness of software-based fault tolerance. Furthermore, software-based fault injection cannot accurately model the relative improvement provided by fault tolerant software implementation, and hence, its results could be misleading.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01444612
Contributor : Caroline Lebrun <>
Submitted on : Tuesday, January 24, 2017 - 11:26:43 AM
Last modification on : Wednesday, July 24, 2019 - 5:16:25 PM

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Mojtaba Ebrahimi, Maryam Rashvand, Firas Kaddachi, Mehdi B. Tahoori, Giorgio Di Natale. Revisiting software-based soft error mitigation techniques via accurate error generation and propagation models. IOLTS: International On-Line Testing Symposium, Jul 2016, Sant Feliu de Guixols, Spain. pp.66-71, ⟨10.1109/IOLTS.2016.7604674⟩. ⟨lirmm-01444612⟩

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