Hierarchical Temporal Memory using Memristor Networks: A Survey

dc.contributor.authorKrestinskaya, Olga
dc.contributor.authorDolzhikova, Irina
dc.contributor.authorJames, Alex Pappachen
dc.date.accessioned2019-10-29T08:32:15Z
dc.date.available2019-10-29T08:32:15Z
dc.date.issued2018-09-24
dc.descriptionhttps://ieeexplore.ieee.org/document/8471012en_US
dc.description.abstractThis paper presents a survey of the currently available hardware designs for implementation of the human cortex inspired algorithm, Hierarchical Temporal Memory (HTM). In this review, we focus on the state of the art advances of memristive HTM implementation and related HTM applications. With the advent of edge computing, HTM can be a potential algorithm to implement on-chip near sensor data processing. The comparison of analog memristive circuit implementations with the digital and mixed-signal solutions are provided. The advantages of memristive HTM over digital implementations against performance metrics such as processing speed, reduced on-chip area and power dissipation are discussed. The limitations and open problems concerning the memristive HTM, such as the design scalability, sneak currents, leakage, parasitic effects, lack of the analog learning circuits implementations and unreliability of the memristive devices integrated with CMOS circuits are also discussed.en_US
dc.identifier.citationKrestinskaya, O., Dolzhikova, I., & James, A. P. (2018). Hierarchical Temporal Memory Using Memristor Networks: A Survey. IEEE Transactions on Emerging Topics in Computational Intelligence, 2(5), 380–395. https://doi.org/10.1109/tetci.2018.2838124en_US
dc.identifier.issn2471-285X
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/4271
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectHierarchical Temporal Memoryen_US
dc.subjectSpatial Pooleren_US
dc.subjectTemporal Memoryen_US
dc.subjectMemristoren_US
dc.subjectSpin-neuronen_US
dc.subjectCrossbaren_US
dc.titleHierarchical Temporal Memory using Memristor Networks: A Surveyen_US
dc.typeArticleen_US
workflow.import.sourcescience

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