Fault-tolerant artificial neural networks

Jung H. Kim, C. Lursinsap, Sung kwon Park

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


Summary form only given, as follows. Self-recovery methods in artificial neural networks (ANNs) implemented on a digital VLSI chip were investigated. Fault tolerance is the potential benefit of ANNs that extends beyond the high computation rates facilitated by the massive parallelism. If a faulty neuron or a faulty link occurs in ANNs implemented on a VLSI chip, typically ANNs no longer classify all inputs correctly. The ability of ANNs to achieve fault tolerance is not inherent, but must be built in. Also, the built-in fault-tolerant mechanism must be practical and efficient enough for a VLSI chip implementation. A partial relearning scheme was proposed to achieve fault tolerance. The scheme was applied to only a single neuron level, not entire networks. Therefore, the execution speed of the partial relearning will be much faster than that of the normal learning. Furthermore, the partial relearning can be executed in a parallel fashion.

Original languageEnglish
Title of host publicationProceedings. IJCNN - International Joint Conference on Neural Networks
Editors Anon
PublisherPubl by IEEE
Number of pages1
ISBN (Print)0780301641
StatePublished - 1992
EventInternational Joint Conference on Neural Networks - IJCNN-91-Seattle - Seattle, WA, USA
Duration: 1991 Jul 81991 Jul 12

Publication series

NameProceedings. IJCNN - International Joint Conference on Neural Networks


OtherInternational Joint Conference on Neural Networks - IJCNN-91-Seattle
CitySeattle, WA, USA


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