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A Diagonally Weighted Binary Memristor Crossbar Architecture Based on Multilayer Neural Network for Better Accuracy Rate in Speech Recognition ApplicationVO, M.-H.   |
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Author keywords
pattern recognition, memristors, neural network, neural network hardware, speech recognition
References keywords
neural(19), memristor(10), netw(7), networks(6), crossbar(6), circuit(6), recognition(5), network(5), multilayer(5), circuits(5)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2019-05-31
Volume 19, Issue 2, Year 2019, On page(s): 75 - 82
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2019.02010
Web of Science Accession Number: 000475806300010
SCOPUS ID: 85066310486
Abstract
A novel binary memristor crossbar architecture based on multilayer neural networks is proposed in the speech recognition application. Here, the memristor crossbar circuit acts as the weights of the neural network combined with the activation function circuit to determine the output. In the new crossbar architecture, the weights are arranged diagonally and divided into 2 arrays according to positive and negative weights. A speech recognition application for 5 vowels is implemented using the proposed architecture. The result shows that the average recognition rate achieves from 94 percent to 96.6 percent over 1000 audio samples. A statistical table shows that the recognition rate and the number of the memristors increase correspondingly to the number of used bits. From the Monte Carlo simulation, the recognition rate of the proposed binary memristor crossbar is decreased slightly from 94 percent to 93.7 percent, while the memristance variation is increased from 1 percent to 15 percent. |
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
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Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
