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

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...

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Bibliographic Details
Published in:Frontiers in nanotechnology 2021-05, Vol.3
Main Authors: Lilak, Sam, Woods, Walt, Scharnhorst, Kelsey, Dunham, Christopher, Teuscher, Christof, Stieg, Adam Z., Gimzewski, James K.
Format: Article
Language:English
Subjects:
atomic switch networks
in-materio
memristive
neuromorphic
reservoir computing
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Summary: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.
ISSN:2673-3013
2673-3013
DOI:10.3389/fnano.2021.675792