Memristive Operational Amplifiers

dc.contributor.authorIbrayev, Timur
dc.contributor.authorFedorova, Irina
dc.contributor.authorMaan, Akshay Kumar
dc.contributor.authorJames, Alex Pappachen
dc.creatorTimur, Ibrayev
dc.date.accessioned2017-12-14T05:41:56Z
dc.date.available2017-12-14T05:41:56Z
dc.date.issued2014-01-01
dc.description.abstractAbstract 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.identifierDOI:10.1016/j.procs.2014.11.092
dc.identifier.citationTimur Ibrayev, Irina Fedorova, Akshay Kumar Maan, Alex Pappachen James, Memristive Operational Amplifiers, In Procedia Computer Science, Volume 41, 2014, Pages 114-119en_US
dc.identifier.issn18770509
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S1877050914015373
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/2890
dc.language.isoenen_US
dc.publisherProcedia Computer Scienceen_US
dc.relation.ispartofProcedia Computer Science
dc.rights.licenseCopyright © 2014 The Authors. Published by Elsevier B.V.
dc.subjectNeuromorphic computingen_US
dc.subjectThreshold Logicen_US
dc.subjectMemristorsen_US
dc.subjectAmplifiersen_US
dc.titleMemristive Operational Amplifiersen_US
dc.typeArticleen_US
elsevier.aggregationtypeJournal
elsevier.coverdate2014-01-01
elsevier.coverdisplaydate2014
elsevier.endingpage119
elsevier.identifier.doi10.1016/j.procs.2014.11.092
elsevier.identifier.eid1-s2.0-S1877050914015373
elsevier.identifier.piiS1877-0509(14)01537-3
elsevier.identifier.scopusid84939227169
elsevier.issue.name5th Annual International Conference on Biologically Inspired Cognitive Architectures, 2014 BICA
elsevier.openaccess1
elsevier.openaccessarticletrue
elsevier.openaccessuserlicensehttp://creativecommons.org/licenses/by-nc-nd/3.0/
elsevier.openarchivearticlefalse
elsevier.startingpage114
elsevier.teaserThe 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.volume41
workflow.import.sourcescience

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