“…The uncovered PEs on the borders can be fully tested in a top-off run.4) Functional test generation: Functional test generation aims at generating inputs, e.g., images, that are capable of sensitizing the fault and propagating its effect to the output, leading to a different prediction with respect to that of the nominal fault-free network. This approach has been demonstrated for ANNs[127],[146]-[149], including memristive crossbar array-based architectures[146],[147],[149], and for SNNs[150],[151]. As shown in Fig.16, functional tests could be original images from training and testing sets, adversarial examples generated from original images, or synthetic images generated from original images.More specifically, starting from the available set of input samples, one approach is to select samples that are profoundly similar to other samples belonging to different output classes, i.e., a similarity metric could be average pixel intensity[127].…”