Spoken Digit Classification by In-Materio Reservoir Computing with Neuromorphic Atomic Switch Networks
Frontiers in Nanotechnology
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.
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Lilak, S., Woods, W., Scharnhorst, K., Dunham, C., Teuscher, C., Stieg, A. Z., & Gimzewski, J. K. (2021). Spoken Digit Classification by In-Materio Reservoir Computing With Neuromorphic Atomic Switch Networks. Frontiers in Nanotechnology, 3, 675792. https://doi.org/10.3389/fnano.2021.675792