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<p><b>Abstract</b>—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.</p>
Neural architecture, redundant binary representation, three-rail logic, concurrent error detection, unidirectional errors, high-performance architecture.

S. Bettola and V. Piuri, "High Performance Fault-Tolerant Digital Neural Networks," in IEEE Transactions on Computers, vol. 47, no. , pp. 357-363, 1998.
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