GPUs have become the defacto hardware devices to accelerate Deep Neural Network
(DNN) inference in deep learning (DL) frameworks. However, the conventional sequential …

In this article, we investigate the interest in applying a mixed-criticality approach to schedule
convolutional neural network (CNN) applications on multicore architectures. We deal with …

Recently, deep neural networks (DNNs) have been utilized in real-time systems such as
autonomous vehicles, where meeting temporal constraints is essential. However, executing …

Deep neural networks (DNNs) are widely used in autonomous driving due to their high
accuracy for perception, decision, and control. Predictability of the perception module is …

Due to the unprecedented computing requirement of autonomous driving applications, in-
vehicle computing architecture is going under tremendous changes. For that, emerging …

Recent advances in sensing, electronic, processing, machine learning, and communication
technologies are accelerating the development of assisted and automated functions for …

Predictability is an essential challenge for autonomous vehicles' safety. Deep neural
networks have been widely deployed in the AV's perception pipeline. However, it is still an …

In this article, we investigate the interest in applying a mixedcriticality approach to schedule
convolutional neural network (CNN) applications on multicore architectures. We deal with …