The low communication bandwidth between memory and processing units in conventional
von Neumann machines does not support the requirements of emerging applications that …

Hardware realization of artificial intelligence (AI) requires new design styles and even
underlying technologies than those used in traditional digital processors or logic circuits …

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

Prior works typically conducted the fault analysis of neural network accelerator computing
arrays with simulation and focused on the prediction accuracy loss of the neural network …

In this study, a circuit technique and training algorithm that minimizes the effect of stuck-at-
faults (SAFs) within a memristor crossbar array of neural networks (NNs) are presented. To …

The growing popularity of convolutional neural networks (CNNs) has led to the search for
efficient computational platforms to accelerate CNN training. Resistive random-access …

Graph Neural Networks (GNNs) have achieved remarkable accuracy in cognitive tasks such
as predictive analytics on graph-structured data. Hence, they have become very popular in …

Because deep neural networks (DNNs) rely on a large number of parameters and
computations, their implementation in energy-constrained systems is challenging. In this …

In this work, a non-idealities aware software-hardware co-design framework for deep neural
network (DNN) implemented on memristive crossbar is presented. The device level non …

The widespread adoption of deep neural networks in safety-critical systems necessitates the
examination of the safety issues raised by hardware errors. The appropriateness of the …