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Memristive Operational Amplifiers

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dc.contributor.author Ibrayev, Timur
dc.contributor.author Fedorova, Irina
dc.contributor.author Maan, Akshay Kumar
dc.contributor.author James, Alex Pappachen
dc.creator Timur, Ibrayev
dc.date.accessioned 2017-12-14T05:41:56Z
dc.date.available 2017-12-14T05:41:56Z
dc.date.issued 2014-01-01
dc.identifier DOI:10.1016/j.procs.2014.11.092
dc.identifier.citation Timur Ibrayev, Irina Fedorova, Akshay Kumar Maan, Alex Pappachen James, Memristive Operational Amplifiers, In Procedia Computer Science, Volume 41, 2014, Pages 114-119 en_US
dc.identifier.issn 18770509
dc.identifier.uri https://www.sciencedirect.com/science/article/pii/S1877050914015373
dc.identifier.uri http://nur.nu.edu.kz/handle/123456789/2890
dc.description.abstract Abstract The neuronal algorithms process the information coming from the natural environment in analog domain at sensory processing level and convert the signals to digital domain before performing cognitive processing. The weighting of the signals is an inherent way the neurons tell the brain on the importance of the inputs and digitisation using threshold logic the neurons way to make low level decisions from it. The analogue implementation of the weighted multiplication to input responses is essentially an amplification operation and so is the threshold logic comparator that can be implemented using amplifiers. In this sense, amplifiers are essential building in the development of threshold logic computing architectures. Specifically, operational amplifier would act as the best candidate for use with threshold logic circuits due to its useful properties of large gain, low output resistance and high input resistance. In this paper, a reconfigurable operational amplifier is proposed based on quantised conductance devices in combination with MOSFET devices. The designed amplifier is used to design a threshold logic cell that has the capability to work as different logic gates. The presented quantised conductance memristive operational amplifier show promising performance results in terms of power dissipation, on-chip area and THD values. en_US
dc.language.iso en en_US
dc.publisher Procedia Computer Science en_US
dc.relation.ispartof Procedia Computer Science
dc.subject Neuromorphic computing en_US
dc.subject Threshold Logic en_US
dc.subject Memristors en_US
dc.subject Amplifiers en_US
dc.title Memristive Operational Amplifiers en_US
dc.type Article en_US
dc.rights.license Copyright © 2014 The Authors. Published by Elsevier B.V.
elsevier.identifier.doi 10.1016/j.procs.2014.11.092
elsevier.identifier.eid 1-s2.0-S1877050914015373
elsevier.identifier.pii S1877-0509(14)01537-3
elsevier.identifier.scopusid 84939227169
elsevier.volume 41
elsevier.issue.name 5th Annual International Conference on Biologically Inspired Cognitive Architectures, 2014 BICA
elsevier.coverdate 2014-01-01
elsevier.coverdisplaydate 2014
elsevier.startingpage 114
elsevier.endingpage 119
elsevier.openaccess 1
elsevier.openaccessarticle true
elsevier.openarchivearticle false
elsevier.openaccessuserlicense http://creativecommons.org/licenses/by-nc-nd/3.0/
elsevier.teaser The neuronal algorithms process the information coming from the natural environment in analog domain at sensory processing level and convert the signals to digital domain before performing cognitive...
elsevier.aggregationtype Journal
workflow.import.source science


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