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

被引：33

作者：

Corti, Elisabetta ^{[1
]}

Cornejo Jimenez, Joaquin Antonio ^{[1
]}

Niang, Kham M. ^{[2
]}

Robertson, John ^{[2
]}

Moselund, Kirsten E. ^{[1
]}

Gotsmann, Bernd ^{[1
]}

Ionescu, Adrian M. ^{[3
]}

Karg, Siegfried ^{[1
]}

机构：

[1] IBM Res Zurich, Ruschlikon, Switzerland

[2] Univ Cambridge, Dept Engn, Cambridge, England

[3] Ecole Polytech Fed Lausanne, Nanoelect Devices Lab, Lausanne, Switzerland

来源：

FRONTIERS IN NEUROSCIENCE | 2021年 / 15卷

关键词：

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

D O I：

10.3389/fnins.2021.628254

中图分类号：

Q189 [神经科学];

学科分类号：

071006 ;

摘要：

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.

引用

页数：12

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