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|Title:||Fault-tolerance capabilities of a software-implemented Hopfield Neural Network||Authors:||Mansour, Wassim
|Affiliations:||Department of Computer Engineering||Keywords:||Recurrent neural nets||Subjects:||Fault-tolerant computing||Issue Date:||2013||Publisher:||IEEE||Part of:||2013 Third International Conference on Communications and Information Technology (ICCIT)||Conference:||International Conference on Communications and Information Technology (ICCIT) (3rd : 19-21 June 2013 : Beirut, Lebanon)||Abstract:||
The associative Hopfield memory is a form of recurrent Artificial Neural Network (ANN) that can be used in applications such as pattern recognition, noise removal, information retrieval, and combinatorial optimization problems. In general, ANNs are considered as intrinsically fault-tolerant. A study of the capability of this algorithm to tolerate transient faults such as bit-flips provoked by the radiation environment is presented. Two software versions of the Hopfield Neural Network (HNN), one original and one fault-tolerant were implemented and executed by a LEON3 processor. Experimental results show the efficiency of the adopted strategy to tolerate faults that were injected at hardware level.
|URI:||https://scholarhub.balamand.edu.lb/handle/uob/559||Ezproxy URL:||Link to full text||Type:||Conference Paper|
|Appears in Collections:||Department of Computer Engineering|
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