As DNNs become increasingly common in mission-critical applications, ensuring their
reliable operation has become crucial. Conventional resilience techniques fail to account for …

Deep learning tasks cover a broad range of domains and an even more extensive range of
applications, from entertainment to extremely safety-critical fields. Thus, Deep Neural …

In recent years, the need for high security with reliability in the wireless network has
tremendously been increased. To provide high security in reliable networks, mobile ad hoc …

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 …

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 …

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

Convolutional Neural Networks (CNNs) are being increasingly used in safety-critical and
high-performance computing systems. As such systems require high levels of resilience to …

Predicting the remaining useful life (RUL) is a critical step before the decision-making
process and developing maintenance strategies. As a result, it is frequently impacted by …

Abstract General Purpose Graphics Processing Units (GPGPUs) have been extensively
used in the last decade as accelerators in high demanding applications, such as multimedia …