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 …

Deep neural network (DNN) training workloads are increasingly susceptible to hardware
failures in datacenters. For example, Google experienced" mysterious, difficult to identify …

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 …

The advent of High-Performance Computing has led to the adoption of Convolutional Neural
Networks (CNNs) in safety-critical applications such as autonomous vehicles. However …

Systolic array has emerged as a prominent archi-tecture for Deep Neural Network (DNN)
hardware accelerators, providing high-throughput and low-latency performance essen-tial …

Semantic segmentation of images is essential for many applications including autonomous
driving and modern DNNs now achieve high accuracy. Automotive systems are safety …

The ImageNet moment was a turning point for Convolutional Neural Networks (CNNs), as it
demonstrated their potential to revolutionize computer vision tasks. This triumph of CNNs …

As Long Short-Term Memory (LSTM) accelerators are increasingly being employed in safety-
critical applications with high-reliability demands, protecting them against errors becomes …

Mimicking biological neurons by focusing on the excitatory/inhibitory decoding performed by
dendritic trees offers an intriguing alternative to the traditional integrate-and-fire McCullogh …