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 …

Deep Learning, and in particular, Deep Neural Network (DNN) is nowadays widely used in
many scenarios, including safety-critical applications such as autonomous driving. In this …

Deep Neural Networks (DNNs) and their accelerators are being deployed ever more
frequently in safety-critical applications leading to increasing reliability concerns. A …

Deep Neural Networks (DNNs) show promising performance in several application
domains, such as robotics, aerospace, smart healthcare, and autonomous driving …

Deep Neural Networks (DNNs) show promising per-formance in several application
domains. Nevertheless, DNN results may be incorrect, not only because of the network …

The reliability of Neural Networks has gained significant attention, prompting efforts to
develop SW-based hardening techniques for safety-critical scenarios. However, evaluating …

Due to their versatility, Deep Neural Networks are becoming increasingly relevant for the
industrial domain. However, there are still challenges hindering their application, such as …

In recent years, Deep Neural Networks (DNNs) have become increasingly present and used
in any field, and they are now a fundamental element for most artificial intelligence …

In the future, more and more systems will adopt AI-based computation in safety-critical
applications. Convolutional Neural Networks (CNNs) are one of the pillars of this AI …

The contemporary surge in employing deep learning, particularly Convolutional Neural
Networks (CNNs), within safety-critical contexts like Autonomous Driver Systems (ADS) …