Hardware realization of artificial intelligence (AI) requires new design styles and even
underlying technologies than those used in traditional digital processors or logic circuits …

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 …

Graphics Processing Unit (GPU) devices and their associated software programming
languages and frameworks can deliver the computing performance required to facilitate the …

Deep learning (DL) reliability is becoming a growing concern, and efficient reliability
assessment approaches are required to meet safety constraints. This article presents a …

Abstract Nowadays, Convolutional Neural Networks (CNNs) are widely used as prediction
models in different fields, with intensive use in real-time safety-critical systems. Recent …

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

Convolutional Neural Networks (CNNs) are currently one of the most widely used predictive
models in machine learning. Recent studies have demonstrated that hardware faults …

Modern Graphics Processing Units (GPUs) demand life expectancy extended to many years,
exposing the hardware to aging (ie, permanent faults arising after the end-of-manufacturing …

Nowadays, the usage of electronic devices running artificial neural networks (ANNs)-based
applications is spreading in our everyday life. Due to their outstanding computational …

Despite the parallelism and sparsity in neural network models, their transfer into hardware
unavoidably makes them susceptible to hardware-level faults. Hardware-level faults can …