论文标题
针对硬件实施的人工神经网络的动态容忍度：一种深度学习方法
Towards Dynamic Fault Tolerance for Hardware-Implemented Artificial Neural Networks: A Deep Learning Approach
论文作者
论文摘要
动态硬件故障的发生可能会严重损害电子电路的功能。特别是对于数字超低功率系统，降低的安全保证金可以增加动态故障的可能性。这项工作研究了一种深入学习方法，以减轻对人工神经网络的动态故障影响。作为理论用例，考虑通过深度自动编码器的图像压缩。评估表明测试损失对测试期间的断层注入率的线性依赖性。如果训练时期的数量足够大，我们的方法与基线网络相比，测试损失降低了2％以上，而无需其他硬件。在测试过程中没有故障的情况下，与参考网络相比，我们的方法还减少了测试损失。
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.
