Deep learning neural networks (DNNs) have been successful in solving a wide range of
machine learning problems. Specialized hardware accelerators have been proposed to …

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 …

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 …

Machine learning is among the greatest advancements in computer science and
engineering and is today used to classify or detect objects, a key feature in autonomous …

As machine learning (ML) becomes pervasive in high performance computing, ML has
found its way into safety-critical domains (eg, autonomous vehicles). Thus the reliability of …

The adoption of deep neural networks (DNNs) in safety-critical domains has engendered
serious reliability concerns. A prominent example is hardware transient faults that are …

As technology scales to lower feature sizes, devices become more susceptible to soft errors.
Soft errors can lead to silent data corruptions (SDCs), seriously compromising the reliability …

As machine learning (ML) has seen increasing adoption in safety-critical domains (eg,
autonomous vehicles), the reliability of ML systems has also grown in importance. While …

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 …

Hardware errors are becoming more prominent with reducing feature sizes, however
tolerating them exclusively in hardware is expensive. Researchers have explored software …