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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]