Spoken Digit Classification by In-Materio Reservoir Computing With Neuromorphic Atomic Switch Networks - Lilak, Sam; Woods, Walt; Scharnhorst, Kelsey; Dunham, Christopher; Teuscher, Christof; Stieg, Adam Z.; Gimzewski, James K. (Frontiers in Nanotechnology, 2021)
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.