Mathematics and Computers in Simulation, volume 177, pages 232-243

Application of the residue number system to reduce hardware costs of the convolutional neural network implementation

Valueva Maria ¹

Nagornov N N ²

Lyakhov Pavel ³

Valuev Georgii ¹

Chervyakov Nikolay ¹

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Department of Applied, Mathematics and Mathematical Modeling, North-Caucasus Federal University, Stavropol, Russia |

Department of Automation and Control Processes, St. Petersburg Electrotechnical University “LETI”, St. Petersburg, Russia |

Department of Applied Mathematics and Mathematical Modeling, North-Caucasus Federal University, Stavropol, Russia |

Publication type: Journal Article

Publication date: 2020-11-01

Elsevier

Journal: Mathematics and Computers in Simulation

Quartile SCImago

Quartile WOS

Impact factor: 4.6

ISSN: 03784754

DOI: 10.1016/j.matcom.2020.04.031

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Applied Mathematics

Theoretical Computer Science

General Computer Science

Numerical Analysis

Modeling and Simulation

Abstract

Convolutional neural networks are a promising tool for solving the problem of pattern recognition. Most well-known convolutional neural networks implementations require a significant amount of memory to store weights in the process of learning and working. We propose a convolutional neural network architecture in which the neural network is divided into hardware and software parts to increase performance and reduce the cost of implementation resources. We also propose to use the residue number system (RNS) in the hardware part to implement the convolutional layer of the neural network. Software simulations using Matlab 2018b showed that convolutional neural network with a minimum number of layers can be quickly and successfully trained. The hardware implementation of the convolution layer shows that the use of RNS allows to reduce the hardware costs on 7.86%–37.78% compared to the two’s complement implementation. The use of the proposed heterogeneous implementation reduces the average time of image recognition by 41.17%.

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World Scientific	World Scientific, 2, 0.72% World Scientific 2 publications, 0.72%
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Taylor & Francis	Taylor & Francis, 2, 0.72% Taylor & Francis 2 publications, 0.72%
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SPIE	SPIE, 1, 0.36% SPIE 1 publication, 0.36%
American Physical Society (APS)	American Physical Society (APS), 1, 0.36% American Physical Society (APS) 1 publication, 0.36%
Thomas Telford	Thomas Telford, 1, 0.36% Thomas Telford 1 publication, 0.36%
Emerald	Emerald, 1, 0.36% Emerald 1 publication, 0.36%
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PagePress	PagePress, 1, 0.36% PagePress 1 publication, 0.36%
American College of Allergy, Asthma, & Immunology	American College of Allergy, Asthma, & Immunology, 1, 0.36% American College of Allergy, Asthma, & Immunology 1 publication, 0.36%
Optical Society of America	Optical Society of America, 1, 0.36% Optical Society of America 1 publication, 0.36%
Royal Society of Chemistry (RSC)	Royal Society of Chemistry (RSC), 1, 0.36% Royal Society of Chemistry (RSC) 1 publication, 0.36%
BMJ	BMJ, 1, 0.36% BMJ 1 publication, 0.36%
Pleiades Publishing	Pleiades Publishing, 1, 0.36% Pleiades Publishing 1 publication, 0.36%
ifmbe proceedings	ifmbe proceedings, 1, 0.36% ifmbe proceedings 1 publication, 0.36%

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Valueva M. et al. Application of the residue number system to reduce hardware costs of the convolutional neural network implementation // Mathematics and Computers in Simulation. 2020. Vol. 177. pp. 232-243.

GOST all authors (up to 50) Copy

Valueva M., Nagornov N. N., Lyakhov P., Valuev G., Chervyakov N. Application of the residue number system to reduce hardware costs of the convolutional neural network implementation // Mathematics and Computers in Simulation. 2020. Vol. 177. pp. 232-243.

RIS |

Cite this

RIS Copy

TY - JOUR

DO - 10.1016/j.matcom.2020.04.031

UR - https://doi.org/10.1016%2Fj.matcom.2020.04.031

TI - Application of the residue number system to reduce hardware costs of the convolutional neural network implementation

T2 - Mathematics and Computers in Simulation

AU - Valueva, Maria

AU - Nagornov, N N

AU - Lyakhov, Pavel

AU - Valuev, Georgii

AU - Chervyakov, Nikolay

PY - 2020

DA - 2020/11/01 00:00:00

PB - Elsevier

SP - 232-243

VL - 177

SN - 0378-4754

ER -

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Cite this

BibTex Copy

@article{2020_Valueva,

author = {Maria Valueva and N N Nagornov and Pavel Lyakhov and Georgii Valuev and Nikolay Chervyakov},

title = {Application of the residue number system to reduce hardware costs of the convolutional neural network implementation},

journal = {Mathematics and Computers in Simulation},

year = {2020},

volume = {177},

publisher = {Elsevier},

month = {nov},

url = {https://doi.org/10.1016%2Fj.matcom.2020.04.031},

pages = {232--243},

doi = {10.1016/j.matcom.2020.04.031}

}

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Elsevier

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Mathematics and Computers in Simulation

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03784754 (Print)

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