Self-stabilizing microprocessor analyzing and overcoming soft-errors (Extended abstract)

Shlomi Dolev, Yinnon A. Haviv

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

8 Scopus citations

Abstract

Soft-errors are changes in memory value caused by cosmic rays. Decrease in computing features size, decrease in power usage and shorting the micro-cycle period, enhances the influence of soft-errors. Self-stabilizing systems is designed to be started in an arbitrary, possibly corrupted state, due to, say, soft errors, and to converge to a desired behavior. Self-stabilization is defined by the state space of the components, and essentially is a well founded, clearly defined, form of the terms: self-healing, automatic-recovery, automatic-repair, and autonomic-computing. To implement a self-stabilizing system one needs to ensure that the micro-processor that executes the program is self-stabilizing. The self-stabilizing microprocessor copes with any combination of soft errors, converging to perform fetch-decode-execute in fault free periods. Still, it is important that the micro-processor will avoid convergence periods as possible, by masking the effect of soft errors immediately. In this work we present design schemes for self-stabilizing microprocessor, and a new technique for analyzing the effect of soft errors. Previous schemes for analyzing the effect of soft errors were based on simulations. In contrast, our scheme computes lower bound on the micro-processor reliability and enables the micro-processor designer to evaluate the reliability of the design, and to identify reliability bottlenecks.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsChristian Müller-Schloer, Theo Ungerer, Bernhard Bauer
PublisherSpringer Verlag
Pages31-46
Number of pages16
ISBN (Print)3540212388, 9783540212386
DOIs
StatePublished - 1 Jan 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2981
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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