NEURO-MEMRISTIVE CIRCUITS FOR EDGE COMPUTING: A REVIEW

dc.contributor.authorKrestinskaya, Olga
dc.contributor.authorJames, Alex
dc.contributor.authorChua, Leon O.
dc.date.accessioned2022-07-19T05:24:58Z
dc.date.available2022-07-19T05:24:58Z
dc.date.issued2018
dc.description.abstractThe volume, veracity, variability and velocity of data produced from the ever increasing network of sensors connected to Internet pose challenges for power management, scalability and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuro morphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks and open problems in the field of neuro-memristive circuits for edge computing.en_US
dc.identifier.citationKrestinskaya, O., James, A. P., & Chua, L. O. (2020). Neuromemristive Circuits for Edge Computing: A Review. IEEE Transactions on Neural Networks and Learning Systems, 31(1), 4–23. https://doi.org/10.1109/tnnls.2019.2899262en_US
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/6450
dc.language.isoenen_US
dc.publisherIEEE Transactions on Neural Networks and Learning Systemsen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectType of access: Open Accessen_US
dc.subjectMemristorsen_US
dc.subjectMemristor circuitsen_US
dc.subjectNeural Networksen_US
dc.subjectCellular neural networken_US
dc.subjectConvolutional neural networken_US
dc.subjectLong short-term memoryen_US
dc.subjectHierarchical temporal memoryen_US
dc.subjectSpiking neural networksen_US
dc.subjectDeep neural networksen_US
dc.titleNEURO-MEMRISTIVE CIRCUITS FOR EDGE COMPUTING: A REVIEWen_US
dc.typeArticleen_US
workflow.import.sourcescience

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