In this paper, we revisit the system vulnerability stack for transient faults. We reveal severe
pitfalls in widely used vulnerability measurement approaches, which separate the hardware …

We present a resilience analysis framework, called FIdelity, to accurately and quickly
analyze the behavior of hardware errors in deep learning accelerators. Our framework …

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 …

We propose AVGI, a new Statistical Fault Injection (SFI)-based methodology, which delivers
orders of magnitude faster assessment of the Architectural Vulnerability Factor (AVF) of a …

As Convolutional Neural Networks (CNNs) are increasingly being employed in safety-critical
applications, it is important that they behave reliably in the face of hardware errors. Transient …

Graphics Processing Units (GPUs) have rapidly evolved to enable energy-efficient data-
parallel computing for a broad range of scientific areas. While GPUs achieve exascale …

Reliability evaluation of a microprocessor design may reveal vulnerable silicon areas that
require protection against faults, but also hardware structures that are inherently more …

The Arm Triple Core Lock-Step (TCLS) architecture is the natural evolution of Arm Cortex-R
Dual Core Lock-Step (DCLS) processors to increase dependability, predictability, and …

Recent studies have shown that technology and voltage scaling are expected to increase
the likelihood that particle-induced soft errors manifest as multiple-bit errors. This raises …