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)

Abstract

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
Pages1890-1894
Number of pages5
ISBN (Electronic)9781479982974, ISSN: 1058-6393
ISBN (Print)9781479982950
DOIs
Publication statusPublished - 24 Apr 2015
Event48th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, United States
Duration: 02 Nov 201405 Nov 2014

Conference

Conference48th Asilomar Conference on Signals, Systems and Computers
CountryUnited States
CityPacific Grove
Period02/11/201405/11/2014

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing

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  • Cite this

    Roth, C., Studer, C., Karakonstantis, G., & Burgi, A. (2015). Statistical data correction for unreliable memories. In Conference Record - Asilomar Conference on Signals, Systems and Computers (pp. 1890-1894). [7094797] IEEE Computer Society. https://doi.org/10.1109/ACSSC.2014.7094797