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Introduction to noise-resilient computing
Noise abatement is the key problem of small-scaled circuit design. New computational paradigms are needed -- as these circuits shrink, they become very vulnerable to noise and soft errors. In this lecture, we present a probabilistic computation framework for improving the resiliency of logic gates a...
Autores principales: | , , |
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Lenguaje: | eng |
Publicado: |
Morgan & Claypool Publ.
2013
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Materias: | |
Acceso en línea: | http://cds.cern.ch/record/2122819 |
_version_ | 1780949490469961728 |
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author | Yanushkevich, Svetlana N Kasai, Seiya Tangim, Golam |
author_facet | Yanushkevich, Svetlana N Kasai, Seiya Tangim, Golam |
author_sort | Yanushkevich, Svetlana N |
collection | CERN |
description | Noise abatement is the key problem of small-scaled circuit design. New computational paradigms are needed -- as these circuits shrink, they become very vulnerable to noise and soft errors. In this lecture, we present a probabilistic computation framework for improving the resiliency of logic gates and circuits under random conditions induced by voltage or current fluctuation. Among many probabilistic techniques for modeling such devices, only a few models satisfy the requirements of efficient hardware implementation -- specifically, Boltzman machines and Markov Random Field (MRF) models. These |
id | cern-2122819 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2013 |
publisher | Morgan & Claypool Publ. |
record_format | invenio |
spelling | cern-21228192021-04-21T19:52:20Zhttp://cds.cern.ch/record/2122819engYanushkevich, Svetlana NKasai, SeiyaTangim, GolamIntroduction to noise-resilient computingEngineeringNoise abatement is the key problem of small-scaled circuit design. New computational paradigms are needed -- as these circuits shrink, they become very vulnerable to noise and soft errors. In this lecture, we present a probabilistic computation framework for improving the resiliency of logic gates and circuits under random conditions induced by voltage or current fluctuation. Among many probabilistic techniques for modeling such devices, only a few models satisfy the requirements of efficient hardware implementation -- specifically, Boltzman machines and Markov Random Field (MRF) models. TheseMorgan & Claypool Publ.oai:cds.cern.ch:21228192013 |
spellingShingle | Engineering Yanushkevich, Svetlana N Kasai, Seiya Tangim, Golam Introduction to noise-resilient computing |
title | Introduction to noise-resilient computing |
title_full | Introduction to noise-resilient computing |
title_fullStr | Introduction to noise-resilient computing |
title_full_unstemmed | Introduction to noise-resilient computing |
title_short | Introduction to noise-resilient computing |
title_sort | introduction to noise-resilient computing |
topic | Engineering |
url | http://cds.cern.ch/record/2122819 |
work_keys_str_mv | AT yanushkevichsvetlanan introductiontonoiseresilientcomputing AT kasaiseiya introductiontonoiseresilientcomputing AT tangimgolam introductiontonoiseresilientcomputing |