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 …

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

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 …

Rapid progress in the CMOS technology for the past 25 years has increased the
vulnerability of processors towards faults. Subsequently, focus of computer architects shifted …

Extensive research efforts are being carried out to evaluate and improve the reliability of
computing devices either through beam experiments or simulation-based fault injection …

Microprocessor power consumption and dependability are both crucial challenges that
designers have to cope with due to shrinking feature sizes and increasing transistor counts …

Several hyperscalers have recently disclosed the occurrence of Silent Data Corruptions
(SDCs) in their systems fleets, sparking concerns about the severity of known and the …

The complexity of both hardware and software makes GPUs reliability evaluation extremely
challenging. A low level fault injection on a GPU model, despite being accurate, would take …

Chip manufacturers and hyperscalers are becoming increasingly aware of the problem
posed by Silent Data Errors (SDE) and are taking steps to address it. Major computing …