The reliability assessment of systems powered by artificial intelligence (AI) is becoming a crucial step prior to their deployment in safety and mission-critical systems. Recently, many efforts have been made to develop sophisticated techniques to evaluate and improve the resilience of AI models against the occurrence of random hardware faults. However, due to the intrinsic nature of such models, the comparison of the results obtained in state-of-the-art works is crucial, as reference models are missing. Moreover, their resilience is strongly influenced by the training process, the adopted framework and data representation, and so on. To enable a common ground for future research targeting CNN resilience analysis/hardening, this work proposes a first benchmark suite of DL models commonly adopted in this context, providing the models, the training/test data, and the resilience-related information (fault list, coverage, etc.) that can be used as a baseline for fair comparison. To this end, this research identifies a set of axes that have an impact on the resilience and classifies some popular CNN models, in both PyTorch and TensorFlow. Some final considerations are drawn, showing the relevance of a benchmark suite tailored for the resilience context.

Assessing AI systems reliability is essential before deploying them in safety-critical applications. While recent efforts have focused on improving model resilience to random hardware faults, meaningful comparison remains difficult due to the lack of standardized reference models. Different authors use different implementations, which makes comparisons unfair and biased: resilience is influenced by the training processes, the software framework, and data representations. To address these issues, this work introduces a benchmark suite of CNN models to test the resilience of DNNs. The benchmark is structured on different axes: software framework, hardware platform, data representation, task and dataset. It is aimed at providing a shared foundation for fair and reproducible resilience evaluation.

Nowadays, AI applications are becoming extremely popular in our everyday life as well as for the industry. Recent incidents involving hyperscalers have revealed that even cloud-based datacenter hardware can experience failures leading to Silent Data Corruptions (SDCs), also called Silent Data Errors (SDEs). This Special Session delves into the implications of such failures on AI workloads, both during training and inference, and explores methodologies for efficiently detecting SDCs or SDEs through dedicated monitoring phases.

Computational models based on deep learning are today integrated in many safety-critical domains. These algorithms, such as deep neural networks (DNNs), are rapidly growing in size, reaching billions or even trillions of parameters. This factor brings big challenges not only for performance goals but also for dependability aspects such as reliability. The larger the model, the more challenging the reliability assessment becomes. It is now crucial to develop new test approaches supported by acceptable computational costs for the detection of random-hardware faults such as permanent faults, which may change the predictions of DNNs. The aim of this paper is to leverage tensor-related metrics to early detect faulty behaviors during the inference of DNNs. This involves calculating metrics applied to tensors across various domains (such as image processing, audio analysis, and regression) on the Output Feature Maps (OFMs) of a layer. This analysis allows knowing in advance the effect that a permanent fault will have on the output of the DNN application. The effectiveness of the approach has been experimentally demonstrated by means of software fault injection campaigns considering faults affecting weights of Convolutional Neural Networks (CNNs), i.e., ResNet20 and MobileNetV2. The quality of the metrics is discussed in terms of the trade-off between energy consumption and the ability to differentiate between critical and non-critical faults.