The increasing complexity and connectivity of automotive systems have raised concerns
about their vulnerability to security breaches. As a result, the integration of formal methods …

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 neural networks (DNNs) are being incorporated in resource-constrained IoT devices,
which typically rely on reduced memory footprint and low-performance processors. While …

Driven by the success of machine learning algorithms for recognizing and identifying
objects, there are significant efforts to exploit convolutional neural networks (CNNs) in edge …

The adoption of RISC-V processors bloomed in recent years, mainly due to its open
standard and free instruction set architecture. However, much remains to help software …

Machine learning (ML) algorithms have provided straightforward solutions to a wide range of
applications. The high computational demand of such algorithms limits their adoption in …

Convolution neural networks (CNNs) have been incorporated into resource-constrained
edge devices to intelligently manage and process local data coming from a variety of …

Machine learning (ML) algorithms are being incorporated in resource-constrained IoT
platforms, which typically rely on reduced memory footprint and low performance …

This article studies the ISA-extension and application-specific soft error sensitivity of the
RISC-V VeeR EH1 commercial processor core from Western Digital. To this end, a modified …

Security and reliability problems in edge devices can become the Achilles' heel for their
massive use in Internet of Things (IoT) systems. While most works address security by …