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Formal Methods for Modelling and Analysis of Single-Event Upsets

Authors: René Rydhof Hansen; Kim Guldstrand Larsen; Mads Chr. Olesen; Erik Ramsgaard Wognsen;

Formal Methods for Modelling and Analysis of Single-Event Upsets

Abstract

When a high-energy particle such as a proton strikes a CPU, the impact may result in the corruption of a data register on the CPU. Such a single-event upset (SEU), in which a random bit is flipped in the content of a data register, can lead to critical errors in the execution of a program. This is particularly problematic for security-or safety-critical systems where such errors may have grave consequences. In this paper we develop a formal semantic framework for easy formal modelling of a large variety of SEUs in a core assembly language capturing the essential features of the ARM assembly language. We use this framework to formally prove the soundness of a static analysis enforcing so-called blue/green separation in a given program. Blue/green separation is a replication based technique for making a program fault-tolerant with respect to data-flow SEUs, however, full coverage requires special hardware support. We further use our semantic framework for deriving program fragments, so-called gadgets, for partial blue/green separation without special hardware. Finally, we illustrate how to apply statistical model checking in our framework to model and quantify faults that go well beyond data-flow SEUs and can provide statistics on the level of fault-tolerance of a program. We use this to provide evidence that our suggested program modifications significantly decrease the probability of such errors going undetected.

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Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
4
Average
Average
Average
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