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https://doi.org/10.1049/pbcs05...
Part of book or chapter of book . 2020 . Peer-reviewed
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Stochastic methods

Authors: Alessandro Savino; Alessandro Vallero; Stefano Di Carlo;

Stochastic methods

Abstract

It is clear from the previous chapters of this book that both fault-injection techniques and analytical approaches for cross-layer reliability analysis have both positive and negative aspects that must be carefully analyzed whenever choosing the best approach to evaluate the reliability of a computing system, and none of them alone represents an optimal solution. With the increasing complexity of future computing systems, analyzing the impact on system reliability of any change in the technology, circuit, microarchitecture and software is a critical and complex design task that requires proper tools and models. The adoption of cross-layer reliability techniques makes this analysis even more complex and challenging. Therefore, there is an increasing interest into stochastic reliability models that are able to combine the benefit of fault-injection techniques at different abstraction levels and analytical approaches such as Register Data Lifetime [1] or Architectural Correct Execution [2-4] analysis into a unified stochastic model that is able to cope with the complexity of the target design together. This is favored by an increasing discussion on the use of these models in the framework of relevant reliability and safety-related standards such as the IEC 61508 [5] and ISO 26262 [6].

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Italy
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Keywords

DIGITAL SYSTEM DESIGN TEST AND VERIFICATION; fault tolerant computing; stochastic processes; software reliability; reliability; complex design task; stochastic methods; critical design task; computing system; unified stochastic model; optimal solution; abstraction levels; target design; system reliability; cross-layer reliability analysis; fault-injection techniques; cross-layer reliability techniques; architectural correct execution; analytical approaches; microarchitecture; stochastic reliability models

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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!
0
Average
Average
Average
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