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SEU fault tolerance in artificial neural networks

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5 Author(s)
Velazco, R. ; Lab. de Genie Inf., LGI/IMAG, Grenoble, France ; Assoum, A. ; Radi, M.E. ; Ecoffet, R.
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In this paper we investigate the robustness of Artificial Neural Networks when encountering transient modification of information bits related to the network operation. These kinds of faults are likely to occur as a consequence of interaction with radiation. Results of tests performed to evaluate the fault tolerance properties of two different digital neural circuits are presented

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Nuclear Science, IEEE Transactions on  (Volume:42 ,  Issue: 6 )