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High Performance Fault-Tolerant Digital Neural Networks
March 1998 (vol. 47 no. 3)
pp. 357-363

Abstract—Efficient implementation of neural networks requires high-performance architectures, while VLSI realization for mission-critical applications must include fault tolerance. Contemporaneous solution of such problems has not yet been completely afforded in the literature. This paper focuses both on data representation to support high-performance neural computation and on error detection to provide the basic information for fault tolerance by using the redundant binary representation with a three-rail logic implementation. Costs and performances are evaluated referring to multilayered feed-forward networks.

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Index Terms:
Neural architecture, redundant binary representation, three-rail logic, concurrent error detection, unidirectional errors, high-performance architecture.
Citation:
Simone Bettola, Vincenzo Piuri, "High Performance Fault-Tolerant Digital Neural Networks," IEEE Transactions on Computers, vol. 47, no. 3, pp. 357-363, March 1998, doi:10.1109/12.660173
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