Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12258/220
Title: A new model to optimize the architecture of a fault-tolerant modular neurocomputer
Authors: Chervyakov, N. I.
Червяков, Н. И.
Lyakhov, P. A.
Ляхов, П. А.
Babenko, M. G.
Бабенко, М. Г.
Lavrinenko, I. N.
Лавриненко, И. Н.
Lavrinenko, A. V.
Лавриненко, А. В.
Deryabin, M. A.
Дерябин, М. А.
Nazarov, A. S.
Назаров, А. С.
Keywords: Digital neuron;Hopfield neural network;Modular neurocomputer;Residue number system (RNS);Very large scale integrated (VLSI) circuit
Issue Date: 2018
Publisher: Elsevier B.V.
Citation: Chervyakov, N.I., Lyakhov, P.A., Babenko, M.G., Lavrinenko, I.N., Lavrinenko, A.V., Deryabin, M.A., Nazarov, A.S. A new model to optimize the architecture of a fault-tolerant modular neurocomputer // Neurocomputing. - 2018. - Volume 303. - pp. 37-46.
Series/Report no.: Neurocomputing
Abstract: In this paper, we present some results on error detection and correction in a modular neurocomputer that are based on redundant residue number systems. The error correction method developed below involves the modified Chinese Remainder Theorem with fractions and uses a Hopfield neural network to correct the errors. The suggested approach eliminates the need for extending the bases of a residue number system, a costly operation required in case of syndrome decoding with error syndromes calculation on the control bases of the system. Also the approach does not utilize the projection method, another costly operation intended to localize errors (i.e., to detect the moduli associated with faulty digits). The well-known procedures mentioned above seem inefficient in terms of practical implementation, as they employ a mixed radix number system: transition to such a system is iterative and may affect the performance of a whole neurocomputer. Owing to the exclusion of these costly operations, the suggested approach significantly simplifies error correction procedures for integer numbers
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