Traditional fault detection/tolerance techniques exploit multiple instances of the nominal processing and then perform a bit-wise comparison of the outputs to detect the occurrence of faults. In specific application scenarios, e.g., image/signal processing, the elaboration has an inherent degree of fault tolerance because it is possible to use the output even in the presence of slight alterations. In these contexts, the classical bit-wise comparison may be inefficient. Indeed, it may lead to conservatively discard outputs that have been only slightly altered by the fault and that could still be usefully exploited. In this paper, we propose a smart checking scheme based on Convolutional Neural Networks that rather than distinguishing between faulty and not faulty images, discriminates between usable and not usable images according to the ability of the end user to correctly process the output. The experimental evaluation shows that this solution enables an execution time saving of about 6.35% with a 99.42% accuracy, on average.

A Smart Fault Detection Scheme for Reliable Image Processing Applications

Matteo Biasielli;Cristiana Bolchini;Luca Cassano;Antonio Miele
2019

Abstract

Traditional fault detection/tolerance techniques exploit multiple instances of the nominal processing and then perform a bit-wise comparison of the outputs to detect the occurrence of faults. In specific application scenarios, e.g., image/signal processing, the elaboration has an inherent degree of fault tolerance because it is possible to use the output even in the presence of slight alterations. In these contexts, the classical bit-wise comparison may be inefficient. Indeed, it may lead to conservatively discard outputs that have been only slightly altered by the fault and that could still be usefully exploited. In this paper, we propose a smart checking scheme based on Convolutional Neural Networks that rather than distinguishing between faulty and not faulty images, discriminates between usable and not usable images according to the ability of the end user to correctly process the output. The experimental evaluation shows that this solution enables an execution time saving of about 6.35% with a 99.42% accuracy, on average.
Proc. Design, Automation and Test in Europe Conference (DATE)
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1090457
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