Design of memristor-based image convolution calculation in convolutional neural network

Zeng, X; Wen, S; Zeng, Z; Huang, T

Design of memristor-based image convolution calculation in convolutional neural network

Zeng, X Wen, S

Zeng, Z Huang, T

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Neural Computing and Applications, 2018, 30, (2), pp. 503-508
Issue Date:: 2018-07-01

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Field	Value	Language
dc.contributor.author	Zeng, X
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.contributor.author	Zeng, Z
dc.contributor.author	Huang, T
dc.date.accessioned	2022-09-04T07:31:14Z
dc.date.available	2022-09-04T07:31:14Z
dc.date.issued	2018-07-01
dc.identifier.citation	Neural Computing and Applications, 2018, 30, (2), pp. 503-508
dc.identifier.issn	0941-0643
dc.identifier.issn	1433-3058
dc.identifier.uri	http://hdl.handle.net/10453/161316
dc.description.abstract	In this paper, an architecture based on memristors is proposed to implement image convolution computation in convolutional neural networks. This architecture could extract different features of input images when using different convolutional kernels. Bipolar memristors with threshold are employed in this work, which vary their conductance values under different voltages. Various kernels are needed to extract information of input images, while different kernels contain different weights. The memristances of bipolar memristors with threshold are convenient to be varied and kept, which make them suitable to act as the weights of kernels. The performances of the design are verified by simulation results.
dc.language	English
dc.publisher	SPRINGER
dc.relation	National Natural Science Foundation of China61403152
dc.relation.ispartof	Neural Computing and Applications
dc.relation.isbasedon	10.1007/s00521-016-2700-2
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Design of memristor-based image convolution calculation in convolutional neural network
dc.type	Journal Article
utslib.citation.volume	30
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2022-09-04T07:31:13Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	30
utslib.citation.issue	2

Abstract:

In this paper, an architecture based on memristors is proposed to implement image convolution computation in convolutional neural networks. This architecture could extract different features of input images when using different convolutional kernels. Bipolar memristors with threshold are employed in this work, which vary their conductance values under different voltages. Various kernels are needed to extract information of input images, while different kernels contain different weights. The memristances of bipolar memristors with threshold are convenient to be varied and kept, which make them suitable to act as the weights of kernels. The performances of the design are verified by simulation results.

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http://hdl.handle.net/10453/161316