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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  2/2019 - 10

A Diagonally Weighted Binary Memristor Crossbar Architecture Based on Multilayer Neural Network for Better Accuracy Rate in Speech Recognition Application

VO, M.-H. See more information about VO, M.-H. on SCOPUS See more information about VO, M.-H. on IEEExplore See more information about VO, M.-H. on Web of Science
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Download PDF pdficon (1,804 KB) | Citation | Downloads: 313 | Views: 741

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

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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.

References | Cited By  «-- Click to see who has cited this paper

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[CrossRef] [Web of Science Times Cited 857]

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[12] M. Hu, H. Li, Y. Chen, Q. Wu, G. S. Rose, and R. W. Linderman, "Memristor Crossbar-Based Neuromorphic Computing System: A Case Study," IEEE Trans. Neural Netw. Learn. Syst., vol. 25, no. 10, pp. 1864-1878, Oct. 2014.
[CrossRef] [Web of Science Times Cited 155]

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[14] S. N. Truong, S.-J. Ham, and K.-S. Min, "Neuromorphic crossbar circuit with nanoscale filamentary-switching binary memristors for speech recognition," Nanoscale Res. Lett., vol. 9, no. 1, p. 629, 2014.
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[15] S. N. Truong, S. Shin, S.-D. Byeon, J. Song, H.-S. Mo, and K.-S. Min, "Comparative Study on Statistical-Variation Tolerance Between Complementary Crossbar and Twin Crossbar of Binary Nano-scale Memristors for Pattern Recognition," Nanoscale Res. Lett., vol. 10, Oct. 2015.
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[19] S. P. Adhikari, H. Kim, R. K. Budhathoki, C. Yang, and L. O. Chua, "A Circuit-Based Learning Architecture for Multilayer Neural Networks With Memristor Bridge Synapses," IEEE Trans. Circuits Syst. Regul. Pap., vol. 62, no. 1, pp. 215-223, Jan. 2015.
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[20] D. Soudry, D. D. Castro, A. Gal, A. Kolodny, and S. Kvatinsky, "Memristor-Based Multilayer Neural Networks With Online Gradient Descent Training," IEEE Trans. Neural Netw. Learn. Syst., vol. 26, no. 10, pp. 2408-2421, Oct. 2015.
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[22] Y. Zhang, X. Wang, and E. G. Friedman, "Memristor-Based Circuit Design for Multilayer Neural Networks," IEEE Trans. Circuits Syst. Regul. Pap., vol. 65, no. 2, pp. 677-686, Feb. 2018.
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[24] L. Muda, M. Begam, and I. Elamvazuthi, "Voice Recognition Algorithms using Mel Frequency Cepstral Coefficient (MFCC) and Dynamic Time Warping (DTW) Techniques," Journal of Computing, vol. 2, no. 3, pp. 138-143, Mar. 2010.

References Weight

Web of Science® Citations for all references: 9,843 TCR
SCOPUS® Citations for all references: 0

Web of Science® Average Citations per reference: 379 ACR
SCOPUS® Average Citations per reference: 0

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-01-18 20:51 in 142 seconds.

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