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Coupled VO2 Oscillators Circuit as Analog First Layer Filter in Convolutional Neural Networks.

Published version
Peer-reviewed

Type

Article

Change log

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

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

Journal Title

Front Neurosci

Conference Name

Journal ISSN

1662-4548
1662-453X

Volume Title

15

Publisher

Frontiers Media SA
Sponsorship
European Commission Horizon 2020 (H2020) Future and Emerging Technologies (FET) (737109)