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dc.contributor.editorLai, Chun Sing
dc.contributor.editorDong, Zhekang
dc.contributor.editorQi, Donglian
dc.date.accessioned2023-04-05T12:51:58Z
dc.date.available2023-04-05T12:51:58Z
dc.date.issued2023
dc.identifierONIX_20230405_9783036566887_83
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/98804
dc.description.abstractThis reprint presents the Special Issue on “Memristive Devices and Systems: Modeling, Properties, and Applications”. The Special Issue provides a comprehensive overview of key computational primitives enabled by these memory devices, as well as their applications, spanning edge computing, signal processing, optimization, machine learning, deep learning, stochastic computing, and so on. The memristor is considered to be a promising candidate for next-generation computing systems due to its nonvolatility, high density, low power, nanoscale geometry, nonlinearity, binary/multiple memory capacity, and negative differential resistance. Novel computing architectures/systems based on memristors have shown great potential to replace the traditional von Neumann computing architecture, which faces data movement challenges. With the development of material science, novel preparation and modeling methods for different memristive devices have been put forward recently, which opens up a new path for realizing different computing systems/architectures with practical memristor properties.
dc.languageEnglish
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issuesen_US
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technologyen_US
dc.subject.othermemristor
dc.subject.otherhistory erase effect
dc.subject.otherdynamic route
dc.subject.otherpower-off plot
dc.subject.otherRRAM
dc.subject.other1T-1R
dc.subject.othermultilevel
dc.subject.othercompact modeling
dc.subject.otherVerilog-A
dc.subject.otherartificial neural network
dc.subject.otherchaos
dc.subject.otherfractional-order calculus
dc.subject.othermemristor model
dc.subject.othercoexisting attractors
dc.subject.otherAdomian decomposition method
dc.subject.otherVO2 carbon nanotube composite memristor
dc.subject.othercellular neural network (CNN)
dc.subject.othervon Neumann structure
dc.subject.otherlocal activity
dc.subject.otheredge of chaos
dc.subject.otheremulator
dc.subject.othergyrator
dc.subject.othermemcapacitor
dc.subject.othermeminductor
dc.subject.othermemristors
dc.subject.othermemristive systems
dc.subject.otherintegrated storage and computation
dc.subject.otherimage processing
dc.subject.otherthe edge of chaos
dc.subject.otherHopfield neural network
dc.subject.othersynaptic crosstalk
dc.subject.othercoexisting dynamics
dc.subject.otheroptoelectronic memristor
dc.subject.othercomposite circuit
dc.subject.othermulti-valued logic
dc.subject.otherrotation mechanism
dc.subject.othern/a
dc.titleMemristive Devices and Systems: Modelling, Properties & Applications
dc.typebook
oapen.identifier.doi10.3390/books978-3-0365-6689-4
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0
oapen.relation.isbn9783036566887
oapen.relation.isbn9783036566894
oapen.pages218
oapen.place.publicationBasel


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