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 …

As machine learning (ML) has seen increasing adoption in safety-critical domains (eg,
autonomous vehicles), the reliability of ML systems has also grown in importance. While …

As CNNs are being extensively employed in high performance and safety-critical
applications that demand high reliability, it is important to ensure that they are resilient to …

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

The great quest for adopting AI-based computation for safety-/mission-critical applications
motivates the interest towards methods for assessing the robustness of the application wrt …

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 …

Learning-based navigation systems are widely used in autonomous applications, such as
robotics, unmanned vehicles and drones. Specialized hardware accelerators have been …

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