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Coupled VO 2 Oscillators Circuit as Analog First Layer Filter in Convolutional Neural Networks

Published version
Peer-reviewed

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Authors

Corti, Elisabetta 
Cornejo Jimenez, Joaquin Antonio 
Niang, Kham M. 
Robertson, John 
Moselund, Kirsten E. 

Abstract

In this work we present an in-memory computing platform based on coupled VO2 oscillators fabricated in a crossbar configuration on silicon. Compared to existing platforms, the crossbar configuration promises significant improvements in terms of area density and oscillation frequency. Further, the crossbar devices exhibit low variability and extended reliability, hence, enabling experiments on 4-coupled oscillator. We demonstrate the neuromorphic computing capabilities using the phase relation of the oscillators. As an application, we propose to replace digital filtering operation in a convolutional neural network with oscillating circuits. The concept is tested with a VGG13 architecture on the MNIST dataset, achieving performances of 95% in the recognition task.

Description

Keywords

Neuroscience, oscillatory neural network, vanadium dioxide, phase-encoding, convolutional neural networks, pattern recognition, relaxation oscillators, coupled oscillators

Journal Title

Frontiers in Neuroscience

Conference Name

Journal ISSN

1662-453X

Volume Title

15

Publisher

Frontiers Media S.A.