Title

Spoken Digit Classification by In-Materio Reservoir Computing with Neuromorphic Atomic Switch Networks

Published In

Frontiers in Nanotechnology

Document Type

Citation

Publication Date

5-26-2021

Abstract

Atomic Switch Networks comprising silver iodide (AgI) junctions, a material previously unexplored as functional memristive elements within highly interconnected nanowire networks, were employed as a neuromorphic substrate for physical Reservoir Computing This new class of ASN-based devices has been physically characterized and utilized to classify spoken digit audio data, demonstrating the utility of substrate-based device architectures where intrinsic material properties can be exploited to perform computation in-materio. This work demonstrates high accuracy in the classification of temporally analyzed Free-Spoken Digit Data These results expand upon the class of viable memristive materials available for the production of functional nanowire networks and bolster the utility of ASN-based devices as unique hardware platforms for neuromorphic computing applications involving memory, adaptation and learning.

DOI

10.3389/fnano.2021.675792

Persistent Identifier

https://archives.pdx.edu/ds/psu/36000

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