Techniques for detecting and masking faults in semantic segmentation applications

Abstract

Semantic segmentation of images is essential for many applications including autonomous driving and modern DNNs now achieve high accuracy. Automotive systems are safety critical systems and in turn they must comply with safety standards, requiring at least hardware fault detection capability. Small embedded applications also require some level of fault tolerance, while operating with a tight power budget. In this paper, we first present a detailed analysis of the effects of faults using Google’s DeepLabV3+ network processing an industrial data-set. Further to that, two techniques to mitigate hardware faults are proposed. The first one is a symptom-based fault detection algorithm shown to detect $>$99% of critical faults with zero false positives and a compute overhead of 0.2%. The second one is a simpler technique, using a clipped ReLU activation function, to quickly mask over 99% of the critical faults in the activation values.