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Title: An efficient method of error correction in fault-tolerant modular neurocomputers
Authors: Chervyakov, N. I.
Червяков, Н. И.
Lyakhov, P. A.
Ляхов, П. А.
Babenko, M. G.
Бабенко, М. Г.
Garyanina, A. I.
Гарянина, А. И.
Lavrinenko, I. N.
Лавриненко, И. Н.
Lavrinenko, A. V.
Лавриненко, А. В.
Deryabin, M. A.
Дерябин, М. А.
Keywords: Artificial neural network;Chinese remainder theorem (CRT);Fault-tolerance;Mixed radix conversion;Residue number system (RNS);Network architecture
Issue Date: 2016
Publisher: Elsevier
Citation: Chervyakov, N.I., Lyakhov, P.A., Babenko, M.G., Garyanina, A.I., Lavrinenko, I.N., Lavrinenko, A.V., Deryabin, M.A. An efficient method of error correction in fault-tolerant modular neurocomputers // Neurocomputing. - 2016. - Volume 205. - Pages 32-44
Series/Report no.: Neurocomputing
Abstract: In this paper, we propose the architecture of a fault-tolerant unit in a modular neurocomputer that is based on decoding with computation of errors syndromes on redundant moduli and implemented using FPGA and a finite ring neural network. The computational complexity of the proposed architecture is about 80% less in comparison with that of the architecture based on number projections in the mixed radix number system
Appears in Collections:Статьи, проиндексированные в SCOPUS, WOS

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