Efficient Identification of Critical Faults in Memristor Crossbars for Deep Neural Networks.

Deep neural networks (DNNs) are becoming ubiquitous, but hardware-level reliability is a concern when DNN models are mapped to emerging neuromorphic technologies such as memristor-based crossbars. As DNN architectures are inherently fault-tolerant and many faults do not affect inferencing accuracy, careful analysis must be carried out to identify faults that are critical for a given application. We present a misclassification-driven training (MDT) algorithm to efficiently identify critical faults (CFs) in the crossbar. Our results for two DNNs on the CIFAR-10 data set show that MDT can rapidly and accurately identify a large number of CFs-up to 20x faster than a baseline method of forward inferencing with randomly injected faults. We use the set of CFs obtained using MDT and the set of benign faults obtained using forward inferencing to train a machine learning (ML) model to efficiently classify all the crossbar faults in terms of their criticality. We show that the ML model can classify millions of faults within minutes with a remarkably high classification accuracy of over 99%. We present a fault-tolerance solution that exploits this high degree of criticality-classification accuracy, leading to a 93% reduction in the redundancy needed for fault tolerance.