Deep learning neural networks (DNNs) have been successful in solving a wide range of
machine learning problems. Specialized hardware accelerators have been proposed to …

Graphics processing units (GPUs) are playing a critical role in convolutional neural networks
(CNNs) for image detection. As GPU-enabled CNNs move into safety-critical environments …

A model-independent sensitivity analysis for (deep) neural network, Bilateral sensitivity
analysis (BiSA), is proposed to measure the relationship or dependency between neurons …

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 …

High accuracy and ever-increasing computing power have made deep neural networks
(DNNs) the algorithm of choice for various machine learning, computer vision, and image …

Neural networks provide a way to realize one of our human dreams to make machines think
like us. Artificial neural networks have been developed since Rosenblatt proposed the …

An important issue in the design and implementation of a neural network is the sensitivity of
its output to input and weight perturbations. In this paper, we discuss the sensitivity of the …

Machine Learning (ML) applications have emerged as the killer applications for next
generation hardware and software platforms, and there is a lot of interest in software …

Safety is the most important aspect of an autonomous driving platform. Deep neural
networks (DNNs) play an increasingly critical role in localization, perception, and control in …

Assessing the reliability of modern devices running CNN algorithms is a very difficult task.
Actually, the complexity of the state-of-the-art devices makes exhaustive Fault Injection (FI) …