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

M.V. Valueva, N.N. Nagornov, P.A. Lyakhov, G.V. Valuev and N.I. Chervyakov

Mathematics and Computers in Simulation (MATCOM), 2020, vol. 177, issue C, 232-243

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%.

Keywords: Image processing; Convolutional neural networks; Residue number system; Quantization noise; Field-programmable gate array (FPGA). (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:177:y:2020:i:c:p:232-243

DOI: 10.1016/j.matcom.2020.04.031

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