Graphics Processing Unit (GPU) devices and their associated software programming
languages and frameworks can deliver the computing performance required to facilitate the …

Real-time computing plays a crucial role in our society since an increasing number of
complex systems rely, in part or completely, on computer control. Examples of applications …

The field of edge computing has grown considerably over the past few years, with
applications in artificial intelligence and big data processing, particularly due to its powerful …

Modern automotive-grade embedded computing platforms feature high-performance
Graphics Processing Units (GPUs) to support the massively parallel processing power …

A fundamental shift is resha** how real-time analysis is applied in all forms of
autonomous 45 systems (eg, UAVs, robotics, and, especially, self-driving automobiles) …

Over the last few years, the ever-increasing use of Graphic Processing Units (GPUs) in
safety-related domains has opened up many research problems in the real-time community …

Recent commercial hardware platforms for embedded real-time systems feature
heterogeneous processing units and computing accelerators on the same System-on-Chip …

Deep learning-based solutions and, in particular, deep neural networks (DNNs) are at the
heart of several functionalities in critical-real time embedded systems (CRTES) from vision …

In embedded systems, CPUs and GPUs typically share main memory. The resulting memory
contention may significantly inflate the duration of CPU tasks in a hard-to-predict way …

OpenVX is a recently ratified standard that was expressly proposed to facilitate the design of
computer-vision (CV) applications used in real-time embedded systems. Despite its real …