AUTHOR=Núñez Juan , Avedillo María J. , Jiménez Manuel , Quintana José M. , Todri-Sanial Aida , Corti Elisabetta , Karg Siegfried , Linares-Barranco Bernabé 

TITLE=Oscillatory Neural Networks Using VO2 Based Phase Encoded Logic

JOURNAL=Frontiers in Neuroscience

VOLUME=15

YEAR=2021

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2021.655823

DOI=10.3389/fnins.2021.655823

ISSN=1662-453X

ABSTRACT=<p>Nano-oscillators based on phase-transition materials are being explored for the implementation of different non-conventional computing paradigms. In particular, vanadium dioxide (VO<sub>2</sub>) devices are used to design autonomous non-linear oscillators from which oscillatory neural networks (ONNs) can be developed. In this work, we propose a new architecture for ONNs in which sub-harmonic injection locking (SHIL) is exploited to ensure that the phase information encoded in each neuron can only take two values. In this sense, the implementation of ONNs from neurons that inherently encode information with two-phase values has advantages in terms of robustness and tolerance to variability present in VO<sub>2</sub> devices. Unlike conventional interconnection schemes, in which the sign of the weights is coded in the value of the resistances, in our proposal the negative (positive) weights are coded using static inverting (non-inverting) logic at the output of the oscillator. The operation of the proposed architecture is shown for pattern recognition applications.</p>