Automatic Test Pattern Generation and Compaction for Deep Neural Networks
Dina Moussa, Michael Hefenbrock, Christopher Münch, Mehdi Baradaran Tahoori · 2023
Deep Neural Networks (DNNs) have gained considerable attention lately due to their excellent performance on a wide range of recognition and classification tasks. Accordingly, fault detection in DNNs and their implementations plays a crucial role in the quality of DNN implementations to ensure that their post-mapping and infield accuracy matches with model accuracy. This paper proposes a functional-level automatic test pattern generation approach for DNNs. This is done by generating inputs which causes misclassification of the output class label in the presence of single or multiple faults. Furthermore, to obtain a smaller set of test patterns with full coverage, a heuristic algorithm as well as a test pattern clustering method using K-means were implemented. The experimental results showed that the proposed test patterns achieved the highest label misclassification and a high output deviation compared to state-of-the-art approaches.