10 papers
Towards On-Board Implementation of ML-Based Helicopter Weight Estimator
Nicolas Valot, Ammar Mechouche, Benjamin Lesage +2
This paper focuses on the implementation of a novel supervised Machine Learning model for estimating helicopter weight during takeoff, utilizing extensive datasets from Airbus's gl…
Compilation and Execution of an Embeddable YOLO-NAS on the VTA
Anthony Faure-Gignoux, Kevin Delmas, Adrien Gauffriau +1
Deploying complex Convolutional Neural Networks (CNNs) on FPGA-based accelerators is a promising way forward for safety-critical domains such as aeronautics. In a previous work, we…
Certified geometric robustness -- Super-DeepG
Noémie Cohen, Mélanie Ducoffe, Christophe Gabreau +2
Safety-critical applications are required to perform as expected in normal operations. Image processing functions are often required to be insensitive to small geometric perturbati…
LARD 2.0: Enhanced Datasets and Benchmarking for Autonomous Landing Systems
Yassine Bougacha, Geoffrey Delhomme, Mélanie Ducoffe +8
This paper addresses key challenges in the development of autonomous landing systems, focusing on dataset limitations for supervised training of Machine Learning (ML) models for ob…
Convolutions Predictable Offloading to an Accelerator: Formalization and Optimization
Benjamin Husson, Mohammed Belcaïd, Thomas Carle +1
Convolutional neural networks (CNNs) require a large number of multiply-accumulate (MAC) operations. To meet real-time constraints, they often need to be executed on specialized ac…
Extension of ACETONE C code generator for multi-core architectures
Yanis Aït-Aïssa, Thomas Carle, Sergei Chichin +2
As the industry's interest in machine learning has grown in recent years, some solutions have emerged to safely embed them in safety-critical systems, such as the C code generator…