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 …

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 …

With the ever-shrinking size of transistors, silent data corruptions (SDCs) are becoming a
common yet serious issue in HPC. Selective instruction duplication (SID) is a widely used …

Fault injection (FI) techniques are typically used to determine the reliability profiles of
programs under soft errors. However, these techniques are highly resource-and time …

Large Language Models (LLMs) are transforming the field of natural language processing
and revolutionizing the way machines interact with humans. LLMs like ChatGPT and …

Motion planning is a computationally intensive and well-studied problem in autonomous
robots. However, motion planning hardware accelerators (MPA) must be soft-error resilient …

Convolutional neural networks (CNNs) have become an established part of numerous safety-
critical computer vision applications, including human robot interactions and automated …

Due to the increasing scale of high-performance computing (HPC) systems, transient
hardware faults have become a major reliability concern. Consequently, Silent Data …

Deep neural network (deepnet) applications play a crucial role in safety-critical systems
such as autonomous vehicles (AVs). An AV must drive safely towards its destination …