18 citations · 29 across the 16 of their papers we have counts for
19 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…
Interferences within a certifiable design methodology for high-performance multi-core platforms
Mohamed Amine Khelassi, Felix Suchert, Abderaouf Amalou +8
The adoption of high-performance multi-core platforms in avionics and automotive systems introduces significant challenges in ensuring predictable execution, primarily due to share…
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…
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…
Implementation of airborne ML models with semantics preservation
Nicolas Valot, Louis Fabre, Benjamin Lesage +2
Machine Learning (ML) may offer new capabilities in airborne systems. However, as any piece of airborne systems, ML-based systems will be required to guarantee their safe operation…
COOK Access Control on an embedded Volta GPU
Benjamin Lesage, Frédéric Boniol, Claire Pagetti
The last decade has seen the emergence of a new generation of multi-core in response to advances in machine learning, and in particular Deep Neural Network (DNN) training and infer…
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…
Certified ML Object Detection for Surveillance Missions
Mohammed Belcaid, Eric Bonnafous, Louis Crison +3
In this paper, we present a development process of a drone detection system involving a machine learning object detection component. The purpose is to reach acceptable performance…
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…
VerifIoU -- Robustness of Object Detection to Perturbations
Noémie Cohen, Mélanie Ducoffe, Ryma Boumazouza +4
We introduce a novel Interval Bound Propagation (IBP) approach for the formal verification of object detection models, specifically targeting the Intersection over Union (IoU) metr…
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…
White Paper Machine Learning in Certified Systems
Hervé Delseny, Christophe Gabreau, Adrien Gauffriau +25
Machine Learning (ML) seems to be one of the most promising solution to automate partially or completely some of the complex tasks currently realized by humans, such as driving veh…
Safety Verification of Neural Network Controlled Systems
Arthur Clavière, Eric Asselin, Christophe Garion +1
In this paper, we propose a system-level approach for verifying the safety of neural network controlled systems, combining a continuous-time physical system with a discrete-time ne…
Note on the exact delay stability margin computation of hybrid dynamical systems
V. Bellet, C. Poussot-Vassal, C. Pagetti +1
Traditionally, the delay margin of a looped system is computed by considering both the controller and system representations that evolve in the same space (e.g. either continuous o…