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 …

Currently, machine learning (ML) techniques are at the heart of smart cyber-physical
systems (CPSs) and Internet-of-Things (loT). This article discusses various challenges and …

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 …

Deep Learning (DL) algorithms have gained popularity owing to their practical problem-
solving capacity. However, they suffer from a serious integrity threat, ie, their vulnerability to …

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

The error-resiliency of Artificial Intelligence (AI) hardware accelerators is a major concern,
especially when they are deployed in mission-critical and safety-critical applications. In this …

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

In this article, we approach to construct reliable deep neural networks (DNNs) for safety-
critical artificial intelligent applications. We propose to modify rectified linear unit (ReLU), a …