IEICE Electronics Express
Online ISSN : 1349-2543
ISSN-L : 1349-2543
On the fault tolerance of a clustered single-electron neural network for differential enhancement
Takahide OyaAlexandre SchmidTetsuya AsaiYusuf LeblebiciYoshihito Amemiya
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2005 Volume 2 Issue 3 Pages 76-80


A clustered neural network, in which neuronal information is represented by a cluster (population of neurons), rather than a single neuron, is a possible solution to construct fault-tolerant single-electron circuits. We designed single-electron circuits based on a clustered neural network that performs differential enhancement where differences between the cluster's outputs receiving various magnitudes of inputs are enhanced after the processing. Simulation results showed that the degradation of the performance of the clustered single-electron neural network was significantly lower than that of a non-clustered network, which indicates that this approach is one possible way to construct fault-tolerant computing systems on nanodevices.

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© 2005 by The Institute of Electronics, Information and Communication Engineers
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