Convolutional neural networks (CNNs) are becoming more and more important for solving
challenging and critical problems in many fields. CNN inference applications have been …

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 …

Neural networks (NNs) are increasingly employed in safety-critical domains and in
environments prone to unreliability (eg, soft errors), such as on spacecraft. Therefore, it is …

Matrix multiplication has always been a cornerstone in computer science. In fact, linear
algebra tools permeate a wide variety of applications: from weather forecasting, to financial …

Managing random hardware faults requires the faults to be detected online, thus simplifying
recovery. Algorithm-based fault tolerance has been proposed as a low-cost mechanism to …

Linear algebra operations have been widely used in big data analytics and scientific
computations. Many works have been done on optimizing linear algebra operations on …

Iterative methods are commonly used approaches to solve large, sparse linear systems,
which are fundamental operations for many modern scientific simulations. When the large …

This work is based on the seminar titled 'Resiliency in Numerical Algorithm Design for
Extreme Scale Simulations' held March 1–6, 2020, at Schloss Dagstuhl, that was attended …

While many algorithm-based fault tolerance (ABFT) schemes have been proposed to detect
soft errors offline in the fast Fourier transform (FFT) after computation finishes, none of the …

Linear algebra operations have been widely used in big data analytics and scientific
computations. Many works have been done on optimizing linear algebra operations on …