  The functionality of electronic circuits can be seriously impaired by the
occurrence of dynamic hardware faults. Particularly, for digital ultra
low-power systems, a reduced safety margin can increase the probability of
dynamic failures. This work investigates a deep learning approach to mitigate
dynamic fault impact for artificial neural networks. As a theoretic use case,
image compression by means of a deep autoencoder is considered. The evaluation
shows a linear dependency of the test loss to the fault injection rate during
testing. If the number of training epochs is sufficiently large, our approach
shows more than 2% reduction of the test loss compared to a baseline network
without the need of additional hardware. At the absence of faults during
testing, our approach also decreases the test loss compared to reference
networks.
