Application of the residue number system to reduce hardware costs of the convolutional neural network implementation
type: Item
instance of: scholarly article
title: Application of the residue number system to reduce hardware costs of the convolutional neural network implementation
author: M. V. Valueva
author: N. N. Nagornov
author: P. A. Lyakhov
author: G. V. Valuev
author: N. I. Chervyakov
published in: Mathematics and Computers in Simulation
publication date: 2021-03-06T00:00:00Z
Mathematics Subject Classification ID: 68Qxx
Mathematics Subject Classification ID: 11Yxx
Mathematics Subject Classification ID: 68Wxx
Mathematics Subject Classification ID: 11Axx
zbMATH DE Number: 7318097
zbMATH Keywords: image processing
zbMATH Keywords: convolutional neural networks
zbMATH Keywords: residue number system
zbMATH Keywords: quantization noise
zbMATH Keywords: field-programmable gate array (FPGA)
describes a project that uses: AlexNet
describes a project that uses: Matlab
describes a project that uses: ImageNet
MaRDI profile type: Publication
full work available at URL: j.matcom.2020.04.031
OpenAlex ID: W3023192521
cites work: Residue-to-binary conversion for general moduli sets based on approximate Chinese remainder theorem
cites work: An approximate sign detection method for residue numbers and its application to RNS division
cites work: Q5424092
cites work: Q5702335
cites work: On Modulo 2^n+1 Adder Design
DOI: 10.1016/J.MATCOM.2020.04.031
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description: scientific article; zbMATH DE number 7318097