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

Deep neural networks (DNNs) have shown remarkable success in various machine-learning
(ML) tasks useful for many safety-critical, real-time embedded systems. The foremost design …

The paper presents a real-time computing framework for intelligent real-time edge services,
on behalf of local embedded devices that are themselves unable to support extensive …

The paper discusses algorithmic priority inversion in mission-critical machine inference
pipelines used in modern neural-network-based cyber-physical applications, and develops …

Object detection is a pivotal research domain within computer vision, with applications
spanning from autonomous vehicles to medical diagnostics. This comprehensive survey …

With the proliferation of applications with machine learning (ML), the importance of edge
platforms has been growing to process streaming sensor, data locally without resorting to …

For realizing safe autonomous driving, the end-to-end delays of real-time object detection
systems should be thoroughly analyzed and minimized. However, despite recent …

On-board computing capacity remains a key bottleneck in modern machine inference
pipelines that run on embedded hardware, such as aboard autonomous drones or cars. To …

This paper explores criticality-based real-time scheduling of neural-network-based machine
inference pipelines in cyber-physical systems (CPS) to mitigate the effect of algorithmic …

Autonomous micromobility systems (AMS) such as low-speed minicabs and robots are
thriving. In AMS, multiple Deep Neural Networks execute in parallel on heterogeneous AI …