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**DOI: **10.18287/2412-6179-2019-43-5-857-868

**Pages:** 857-868.

**Full text of article:** Russian language.

**Abstract**:

Modern convolutional neural networks architectures are very resource intensive which limits the possibilities for their wide practical application. 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 in the hardware part to implement the convolutional layer of the neural network for resource costs reducing. A numerical method for quantizing the filters coefficients of a convolutional network layer is proposed to minimize the influence of quantization noise on the calculation result in the residue number system and determine the bit-width of the filters coefficients. This method is based on scaling the coefficients by a fixed number of bits and rounding up and down. The operations used make it possible to reduce resources in hardware implementation due to the simplifying of their execution. All calculations in the convolutional layer are performed on numbers in a fixed-point format. Software simulations using Matlab 2017b showed that convolutional neural network with a minimum number of layers can be quickly and successfully trained. Hardware implementation using the field-programmable gate array Kintex7 xc7k70tfbg484-2 showed that the use of residue number system in the convolutional layer of the neural network reduces the hardware costs by 32.6% compared with the traditional approach based on the two’s complement representation. The research results can be applied to create effective video surveillance systems, for recognizing handwriting, individuals, objects and terrain.

**Keywords**:

convolutional neural networks, image processing, pattern recognition, residue number system.

**Citation**:

Chervyakov NI, Lyakhov PA, Nagornov NN, Valueva MV, Valuev GV. Hardware implementation of a convolutional neural network using calculations in the residue number system. Computer Optics 2019; 43(5): 857-868. DOI: 10.18287/2412-6179-2019-43-5-857-868.

Acknowledgements:

This work was supported by the Government of the Russian Federation (State order No. 2.6035.2017/BCh), the Russian Foundation for Basic Research (Projects No. 18-07-00109 A, No. 19-07-00130 A and No. 18-37-20059 mol-a-ved), and by the Presidential Grant of the Russian Federation (Project No. SP-2245.2018.5).

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