Memristive Devices for Neuromorphic and Deep Learning Applications

Walters, B.;Lammie, C.;Eshraghian, J.;Yakopcic, C.;Taha, T.;Genov, R.;Jacob, M.V.;Amirsoleimani, A.;Rahimi Azghadi, M.

Abstract

Neuromorphic and deep learning (DL) algorithms are important research areas gaining significant traction of late. Due to this growing interest and the high demand for low-power and high-performance designs for running these algorithms, various circuits and devices are being designed and investigated to realize efficient neuromorphic and DL architectures. One device said to drastically improve this architecture is the memristor. In this chapter, studies investigating memristive implementations into neuromorphic and DL designs are summarized and categorized based on the switching mechanicsms of a few prominent memristive device technologies. Furthermore, the simulation platforms used to model both neuromorphic and DL hardware implementations, which use memristors, are summarized and discussed. This chapter can provide a quick reference for readers interested in learning the latest advancements in the areas of memristive devices and systems for use in neuromorphic and DL systems.

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Advanced Memory Technology: Functional Materials and Devices

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ISBN/ISSN

978-1-83916-995-3

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Publisher

Royal Society of Chemistry

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Publisher Location

London, United Kingdom

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DOI

10.1039/BK9781839169946-00680