Atomic Switch Networks (ASN) 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 (RC). 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 (FSDD). 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.
@article{arxiv.2103.12835,
title = {Spoken Digit Classification by In-Materio Reservoir Computing with Neuromorphic Atomic Switch Networks},
author = {Sam Lilak and Walt Woods and Kelsey Scharnhorst and Christopher Dunham and Christof Teuscher and Adam Z. Stieg and James K. Gimzewski},
journal= {arXiv preprint arXiv:2103.12835},
year = {2021}
}