18 citations · 28 across the 7 of their papers we have counts for
8 papers
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…
Open-source Stand-Alone Versatile Tensor Accelerator
Anthony Faure-Gignoux, Kevin Delmas, Adrien Gauffriau +1
Machine Learning (ML) applications demand significant computational resources, posing challenges for safety-critical domains like aeronautics. The Versatile Tensor Accelerator (VTA…
How to design a dataset compliant with an ML-based system ODD?
Cyril Cappi, Noémie Cohen, Mélanie Ducoffe +8
This paper focuses on a Vision-based Landing task and presents the design and the validation of a dataset that would comply with the Operational Design Domain (ODD) of a Machine-Le…
Towards the Certification of Hybrid Architectures: Analysing Interference on Hardware Accelerators through PML
Benjamin Lesage, Frédéric Boniol, Kevin Delmas +3
The emergence of Deep Neural Network (DNN) and machine learning-based applications paved the way for a new generation of hybrid hardware platforms. Hybrid platforms embed several c…
Formal description of ML models for unambiguous implementation
Adrien Gauffriau, Iryna De Albuquerque Silva, Claire Pagetti
Implementing deep neural networks in safety critical systems, in particular in the aeronautical domain, will require to offer adequate specification paradigms to preserve the seman…
LARD -- Landing Approach Runway Detection -- Dataset for Vision Based Landing
Mélanie Ducoffe, Maxime Carrere, Léo Féliers +4
As the interest in autonomous systems continues to grow, one of the major challenges is collecting sufficient and representative real-world data. Despite the strong practical and c…