A systematic literature review on hardware reliability assessment methods for deep neural networks
Artificial Intelligence (AI) and, in particular, Machine Learning (ML), have emerged to be
utilized in various applications due to their capability to learn how to solve complex …
utilized in various applications due to their capability to learn how to solve complex …
Druto: Upper-bounding silent data corruption vulnerability in gpu applications
Due to the increasing scale of high-performance computing (HPC) systems, transient
hardware faults have become a major reliability concern. Consequently, Silent Data …
hardware faults have become a major reliability concern. Consequently, Silent Data …
Investigating the impact of transient hardware faults on deep learning neural network inference
Safety‐critical applications, such as autonomous vehicles, healthcare, and space
applications, have witnessed widespread deployment of deep neural networks (DNNs) …
applications, have witnessed widespread deployment of deep neural networks (DNNs) …
DeepVigor+: Scalable and Accurate Semi-Analytical Fault Resilience Analysis for Deep Neural Network
Growing exploitation of Machine Learning (ML) in safety-critical applications necessitates
rigorous safety analysis. Hardware reliability assessment is a major concern with respect to …
rigorous safety analysis. Hardware reliability assessment is a major concern with respect to …