Frontiers in Nanotechnology (May 2021)

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

  • Sam Lilak,
  • Walt Woods,
  • Kelsey Scharnhorst,
  • Christopher Dunham,
  • Christof Teuscher,
  • Adam Z. Stieg,
  • Adam Z. Stieg,
  • James K. Gimzewski,
  • James K. Gimzewski,
  • James K. Gimzewski,
  • James K. Gimzewski

DOI
https://doi.org/10.3389/fnano.2021.675792
Journal volume & issue
Vol. 3

Abstract

Read online

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.

Keywords