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Implementation of Dropout Neuronal Units Based on Stochastic Memristive Devices in Neural Networks with High Classification Accuracy
Neural networks based on memristive devices have achieved great progress recently. However, memristive synapses with nonlinearity and asymmetry seriously limit the classification accuracy. Moreover, insufficient number of training samples in many cases also have negative effect on the classification...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7509653/ https://www.ncbi.nlm.nih.gov/pubmed/32999852 http://dx.doi.org/10.1002/advs.202001842 |
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author | Huang, He‐Ming Xiao, Yu Yang, Rui Yu, Ye‐Tian He, Hui‐Kai Wang, Zhe Guo, Xin |
author_facet | Huang, He‐Ming Xiao, Yu Yang, Rui Yu, Ye‐Tian He, Hui‐Kai Wang, Zhe Guo, Xin |
author_sort | Huang, He‐Ming |
collection | PubMed |
description | Neural networks based on memristive devices have achieved great progress recently. However, memristive synapses with nonlinearity and asymmetry seriously limit the classification accuracy. Moreover, insufficient number of training samples in many cases also have negative effect on the classification accuracy of neural networks due to overfitting. In this work, dropout neuronal units are developed based on stochastic volatile memristive devices of Ag/Ta(2)O(5):Ag/Pt. The memristive neural network using the dropout neuronal units effectively solves the problem of overfitting and mitigates the negative effects of the nonideality of memristive synapses, eventually achieves a classification accuracy comparable to the theoretical limit. The stochastic and volatile switching performances of the Ag/Ta(2)O(5):Ag/Pt device are attributed to the stochastical rupture of the Ag filament under high electrical stress in the Ta(2)O(5) layer, according to the TEM observation and the kinetic Monte Carlo simulation. |
format | Online Article Text |
id | pubmed-7509653 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-75096532020-09-29 Implementation of Dropout Neuronal Units Based on Stochastic Memristive Devices in Neural Networks with High Classification Accuracy Huang, He‐Ming Xiao, Yu Yang, Rui Yu, Ye‐Tian He, Hui‐Kai Wang, Zhe Guo, Xin Adv Sci (Weinh) Communications Neural networks based on memristive devices have achieved great progress recently. However, memristive synapses with nonlinearity and asymmetry seriously limit the classification accuracy. Moreover, insufficient number of training samples in many cases also have negative effect on the classification accuracy of neural networks due to overfitting. In this work, dropout neuronal units are developed based on stochastic volatile memristive devices of Ag/Ta(2)O(5):Ag/Pt. The memristive neural network using the dropout neuronal units effectively solves the problem of overfitting and mitigates the negative effects of the nonideality of memristive synapses, eventually achieves a classification accuracy comparable to the theoretical limit. The stochastic and volatile switching performances of the Ag/Ta(2)O(5):Ag/Pt device are attributed to the stochastical rupture of the Ag filament under high electrical stress in the Ta(2)O(5) layer, according to the TEM observation and the kinetic Monte Carlo simulation. John Wiley and Sons Inc. 2020-07-26 /pmc/articles/PMC7509653/ /pubmed/32999852 http://dx.doi.org/10.1002/advs.202001842 Text en © 2020 The Authors. Published by WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Communications Huang, He‐Ming Xiao, Yu Yang, Rui Yu, Ye‐Tian He, Hui‐Kai Wang, Zhe Guo, Xin Implementation of Dropout Neuronal Units Based on Stochastic Memristive Devices in Neural Networks with High Classification Accuracy |
title | Implementation of Dropout Neuronal Units Based on Stochastic Memristive Devices in Neural Networks with High Classification Accuracy |
title_full | Implementation of Dropout Neuronal Units Based on Stochastic Memristive Devices in Neural Networks with High Classification Accuracy |
title_fullStr | Implementation of Dropout Neuronal Units Based on Stochastic Memristive Devices in Neural Networks with High Classification Accuracy |
title_full_unstemmed | Implementation of Dropout Neuronal Units Based on Stochastic Memristive Devices in Neural Networks with High Classification Accuracy |
title_short | Implementation of Dropout Neuronal Units Based on Stochastic Memristive Devices in Neural Networks with High Classification Accuracy |
title_sort | implementation of dropout neuronal units based on stochastic memristive devices in neural networks with high classification accuracy |
topic | Communications |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7509653/ https://www.ncbi.nlm.nih.gov/pubmed/32999852 http://dx.doi.org/10.1002/advs.202001842 |
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