Graphics Processing Unit (GPU) devices and their associated software programming
languages and frameworks can deliver the computing performance required to facilitate the …

Several recent publications have shown that hardware faults in the memory subsystem are
commonplace. These faults are predicted to become more frequent in future systems that …

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 …

As GPUs become more pervasive in both scalable high-performance computing systems
and safety-critical embedded systems, evaluating and analyzing their resilience to soft errors …

Smaller transistor sizes and reduction in voltage levels in modern microprocessors induce
higher soft error rates. This trend makes reliability a primary design constraint for computer …

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 …

This article provides an overview of AMD's vision for exascale computing, and in particular,
how heterogeneity will play a central role in realizing this vision. Exascale computing …

Application execution on safety-critical and high-performance computer systems must be
resilient to transient errors. As GPUs become more pervasive in such systems, they must …

The challenges to push computing to exaflop levels are difficult given desired targets for
memory capacity, memory bandwidth, power efficiency, reliability, and cost. This paper …

Modern Graphics Processing Units (GPUs) demand life expectancy extended to many years,
exposing the hardware to aging (ie, permanent faults arising after the end-of-manufacturing …