Artificial Intelligence (AI) and, in particular, Machine Learning (ML), have emerged to be
utilized in various applications due to their capability to learn how to solve complex …

Ensuring long processor lifetimes by limiting failuresdue to wear-out related hard errors is a
critical requirementfor all microprocessor manufacturers. We observethat continuous device …

Deep neural network (DNN) training workloads are increasingly susceptible to hardware
failures in datacenters. For example, Google experienced" mysterious, difficult to identify …

Abstract Machine Learning (ML) is currently being exploited in numerous applications, being
one of the most effective Artificial Intelligence (AI) technologies used in diverse fields, such …

Winograd is generally utilized to optimize convolution performance and computational
efficiency because of the reduced multiplication operations, but the reliability issues brought …

The advent of High-Performance Computing has led to the adoption of Convolutional Neural
Networks (CNNs) in safety-critical applications such as autonomous vehicles. However …

Deep Neural Networks (DNNs) enable a wide series of technological advancements,
ranging from clinical imaging, to predictive industrial maintenance and autonomous driving …

As Deep Neural Networks (DNNs) are increasingly deployed in safety critical and privacy
sensitive applications such as autonomous driving and biometric authentication, it is critical …

Vision transformers (ViTs) that leverage self-attention mechanism have shown superior
performance on many classical vision tasks compared to convolutional neural networks …

As machine learning (ML) has become more preva-lent across many critical domains, so
has the need to understand ML applications' resilience. While prior work like TensorFI [1] …