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

Deep neural network (DNN) training workloads are increasingly susceptible to hardware
failures in datacenters. For example, Google experienced" mysterious, difficult to identify …

This paper first presents an in-depth study of potential security vulnerabilities in MongoDB
and Cassandra, two popular NoSQL databases. We provide examples of attacks. We then …

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 …

As CNNs are being extensively employed in high performance and safety-critical
applications that demand high reliability, it is important to ensure that they are resilient 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 …

This paper presents Flame, a hardware/software co-designed resilience scheme for
protecting GPUs against soft errors. For low-cost yet high-performance resilience, Flame …

As machine learning (ML) has become more preva-lent across many critical domains, so
has the need to understand ML applications' resilience. While prior work like TensorFI [1] …

Silent data corruption caused by random hardware faults in autonomous vehicle (AV)
computational elements is a significant threat to vehicle safety. Previous research has …

High-Performance Computing (HPC) platforms become a must in automotive systems to
enable autonomous driving. However, automotive platforms must avoid Common Cause …