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 …

The adoption of deep neural networks (DNNs) in safety-critical domains has engendered
serious reliability concerns. A prominent example is hardware transient faults that are …

The security and privacy concerns along with the amount of data that is required to be
processed on regular basis has pushed processing to the edge of the computing systems …

Neural Networks (NNs) are being extensively used in critical applications such as
aerospace, healthcare, autonomous driving, and military, to name a few. Limited precision of …

As deep learning algorithms are widely adopted, an increasing number of them are
positioned in embedded application domains with strict reliability constraints. The …

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

Convolutional neural networks (CNNs) are widely used in computer vision and natural
language processing. Field-programmable gate arrays (FPGAs) are popular accelerators for …

This paper reviews and proposes concerns in adopting, fielding, and maintaining artificial
intelligence (AI) systems. While the AI community has made rapid progress, there are …

Prior works typically conducted the fault analysis of neural network accelerator computing
arrays with simulation and focused on the prediction accuracy loss of the neural network …

Deep learning techniques have enjoyed wide adoption in real life, including in various
safety-critical embedded applications. While neural network computations require protection …