Wide attention was recently given to the problem of fault-tolerance in neural networks; while
most authors dealt with aspects related to specific VLSI implementations, attention was also …

Deep neural network (DNN) accelerators received considerable attention in recent years
due to the potential to save energy compared to mainstream hardware. Low-voltage …

As high-performance computing systems scale in size and computational power, the danger
of silent errors, ie, errors that can bypass hardware detection mechanisms and impact …

An analysis of the influence of weight and input perturbations in a multilayer perceptron
(MLP) is made in this article. Quantitative measurements of fault tolerance, noise immunity …

The sensitivity of a neural network's output to its input perturbation is an important issue with
both theoretical and practical values. In this article, we propose an approach to quantify the …

Video recognition applications running on Graphics Processing Unit are composed of
heterogeneous software portions, such as kernels or layers for neural networks. The authors …

This brief discusses the simple idea that dropout regularization can be used to efficiently
induce resiliency to missing inputs at prediction time in a generic neural network. We show …

Abstract Deep Neural Networks (DNNs) have been successfully deployed in safety-critical
applications due to the capability of computing in complex tasks. Because of low energy …

Many state-of-the-art deep learning models for computer vision tasks are based on the
transformer architecture. Such models can be computationally expensive and are typically …

The use of deep learning (DL) on HPC resources has become common as scientists explore
and exploit DL methods to solve domain problems. On the other hand, in the coming …