Statistical data correction for unreliable memories

Christoph Roth, Christoph Studer, Georgios Karakonstantis, Andreas Burgi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


In this paper, we introduce a statistical data-correction framework that aims at improving the DSP system performance in presence of unreliable memories. The proposed signal processing framework implements best-effort error mitigation for signals that are corrupted by defects in unreliable storage arrays using a statistical correction function extracted from the signal statistics, a data-corruption model, and an application-specific cost function. An application example to communication systems demonstrates the efficacy of the proposed approach.

Original languageEnglish
Title of host publicationConference Record - Asilomar Conference on Signals, Systems and Computers
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781479982974, ISSN: 1058-6393
ISBN (Print)9781479982950
Publication statusPublished - 24 Apr 2015
Event48th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, United States
Duration: 02 Nov 201405 Nov 2014


Conference48th Asilomar Conference on Signals, Systems and Computers
Country/TerritoryUnited States
CityPacific Grove

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing


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