Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/103559
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dc.contributor.authorBerco, Danen
dc.date.accessioned2019-01-03T03:01:55Zen
dc.date.accessioned2019-12-06T21:15:20Z-
dc.date.available2019-01-03T03:01:55Zen
dc.date.available2019-12-06T21:15:20Z-
dc.date.issued2018en
dc.identifier.citationBerco, D. (2018). Rectifying resistive memory devices as dynamic complementary artificial synapses. Frontiers in Neuroscience, 12, 755-. doi:10.3389/fnins.2018.00755en
dc.identifier.issn1662-4548en
dc.identifier.urihttps://hdl.handle.net/10356/103559-
dc.identifier.urihttp://hdl.handle.net/10220/47332en
dc.description.abstractBrain inspired computing is a pioneering computational method gaining momentum in recent years. Within this scheme, artificial neural networks are implemented using two main approaches: software algorithms and designated hardware architectures. However, while software implementations show remarkable results (at high-energy costs), hardware based ones, specifically resistive random access memory (RRAM) arrays that consume little power and hold a potential for enormous densities, are somewhat lagging. One of the reasons may be related to the limited excitatory operation mode of RRAMs in these arrays as adjustable passive elements. An interesting type of RRAM was demonstrated recently for having alternating dynamic switching current rectification properties that may be used for complementary operation much like CMOS transistors. Such artificial synaptic devices may be switched dynamically between excitatory and inhibitory modes to allow doubling of the array density and significantly reducing the peripheral circuit complexity.en
dc.description.sponsorshipMOE (Min. of Education, S’pore)en
dc.format.extent6 p.en
dc.language.isoenen
dc.relation.ispartofseriesFrontiers in Neuroscienceen
dc.rights© 2018 Berco. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.en
dc.subjectBrain Inspired Computingen
dc.subjectArtificial Neural Networksen
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen
dc.titleRectifying resistive memory devices as dynamic complementary artificial synapsesen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.identifier.doihttp://dx.doi.org/10.3389/fnins.2018.00755en
dc.description.versionPublished versionen
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