Memory corruption bugs in software written in low-level languages like C or C++ are one of
the oldest problems in computer security. These unsafe languages are vulnerable to errors …

Diagnosing causes of performance variations in High-Performance Computing (HPC)
systems is a daunting chal-lenge due to the systems' scale and complexity. Variations in …

Deep neural networks (DNNs) have been widely-adopted in various safety-critical
applications such as computer vision and autonomous driving. However, as technology …

Ensuring reliable computation at the nanoscale requires mechanisms to detect and correct
errors during normal circuit operation. In this paper we propose a method for designing …

Due to the centralization of communication in the management of data generated by diverse
Internet of Thing (IoT) devices, there is a lack of reliability when data is being transferred and …

Hardware failures pose critical threats to deep neural network (DNN) training workloads,
and the urgency of tackling this challenge (known as the Silent Data Corruption challenge in …

The vulnerability of the current and future processors towards transient errors caused by
particle strikes is expected to increase rapidly because of exponential growth rate of on-chip …

The accelerated growth of computing systems' complexity makes comprehensive design
verification challenging and time-consuming. In practice, hard-to-model complex …

Today's High Performance Computing (HPC) systems are capable of delivering
performance in the order of petaflops due to the fast computing devices, network …

Anomaly detection, which aims at identifying unexpected trends and data patterns, has
widely been used to build error detectors, failure predictors or intrusion detectors. Internal …