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Convolutional neural network architecture using computations in residue number system with specific module set

Keywords:

N.I. Chervyakov – Dr.Sc. (Eng.), Professor, Head of Department of the Applied Mathematics and Mathematical Modeling, Institute of Mathematics and Natural Sciences, North Caucasus Federal University (Stavropol) E-mail: k-fmf-primath@stavsu.ru P.A. Lyakhov – Ph. D. (Phis.-Math.), Assistant Professor, Department of the Applied Mathematics and Mathematical Modeling, Institute of Mathematics and Natural Sciences, North Caucasus Federal University (Stavropol) E-mail: ljahov@mail.ru D.I. Kalita – Post-graduate Student, Institute of Mathematics and Natural Sciences, North Caucasus Federal University (Stavropol) E-mail: diana.kalita@mail.ru M.V. Valueva – Master Student in «Applied Mathematics and Informatics», Institute of Mathematics and Natural Sciences, North Caucasus Federal University (Stavropol) E-mail: mriya.valueva@mail.ru


Residue Number System (RNS) due to property of parallel computations can be effectively used in the Convolutional Neural Network (CNN) architecture, which also has a parallel structure. Combination of CNN and RNS makes actual a problem of forward and reverse conversion operations realization. The simulation was performed on the FPGA Artix7 XC7A200T in CAD Xilinx ISE Design Suite 14.7. Its purpose was to compare the performance of the known CNN architecture from [12] and proposed CNN architecture. Simulation of forward conversion in proposed architecture shows 21 times faster delay and 56 times less hardware costs than in known architecture. Simulation of reverse conversion in proposed architecture shows 25% faster delay and 32,5% less hardware costs than in known architecture. Analysis of modular adders performance shows that the worst module in proposed specific moduli set is which increase the delay performance of modular adders by 22%. Summarizing these results, we can conclude that the proposed CNN hardware architecture can significantly reduce the time and cost of the most problematic forward and reverse conversions in RNS. The achieved advantage is obtained at the expense of a slight reduction in the efficiency of modular adders in CNN.
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