Walters, B., Lammie, C., Eshraghian, J., Yakopcic, C., Taha, T., Genov, R., Jacob, M.V., Amirsoleimani, A., and Rahimi Azghadi, M. (2023) Memristive Devices for Neuromorphic and Deep Learning Applications. In: Zho, Ye, (ed.) Advanced Memory Technology: Functional Materials and Devices. Optical, Electronic and Magnetic Materials . Royal Society of Chemistry, London, United Kingdom, pp. 680-704.

PDF (Accepted Author Manuscript) - Accepted Version Restricted to Repository staff only until 9 October 2026.

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.

Item ID:	81676
Item Type:	Book Chapter (Research - B1)
ISBN:	978-1-83916-995-3
Copyright Information:	© The Royal Society of Chemistry 2024
Date Deposited:	24 Jan 2024 01:59
FoR Codes:	40 ENGINEERING > 4018 Nanotechnology > 401804 Nanoelectronics @ 50% 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461104 Neural networks @ 50%
SEO Codes:	22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 100%
Downloads:	Total: 2
	More Statistics