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.
翻译:电子电路的功能会因动态硬件故障的发生而严重受损。 特别是,对于数字超低功率系统来说,降低安全幅度可以增加动态故障的概率。 这项工作调查了一种深层次的学习方法,以减轻人工神经网络的动态故障影响。 作为一个理论使用案例,可以考虑用深层自动编码器对图像进行压缩。 评估表明测试损失与测试时的过错注射率具有线性依赖性。 如果培训时代的数量足够大,我们的方法显示测试损失比基线网络减少2%以上,而不需要额外的硬件。 在测试期间没有故障,我们的方法也减少了测试损失与参考网络相比的测试损失。