Deep Neural Networks (DNNs) are widely being adopted for safety-critical applications, eg,
healthcare and autonomous driving. Inherently, they are considered to be highly error …

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 …

Deep neural networks (DNNs) are state-of-the-art algorithms for multiple applications,
spanning from image classification to speech recognition. While providing excellent …

Deep neural networks usually possess a high overall resilience against errors in their
intermediate computations. However, it has been shown that error resilience is generally not …

The widespread usage of deep neural networks in autonomous driving necessitates a
consideration of the safety arguments against hardware-level faults. This study confirms the …

We present a resilience analysis framework, called FIdelity, to accurately and quickly
analyze the behavior of hardware errors in deep learning accelerators. Our framework …

Deep neural networks have become a ubiquitous tool for mastering complex classification
tasks. Current research focuses on the development of power-efficient and fast neural …

As deep learning algorithms are widely adopted, an increasing number of them are
positioned in embedded application domains with strict reliability constraints. The …

Machine Learning (ML) is making a strong resurgence in tune with the massive generation
of unstructured data which in turn requires massive computational resources. Due to the …

The exceptional performance of Deep neural networks (DNNs) encourages their
deployment in safety-and dependability-critical systems. However, DNNs often demonstrate …