arXiv subjects

Claire Pagetti

Publications and source records attributed to Claire Pagetti.

At least 19 recordsLinked to original sources

arXiv2026

Towards On-Board Implementation of ML-Based Helicopter Weight Estimator

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 global in-service fleet. The study details a learning assurance process aligned with the EASA concept paper for machine learning application, and with the on-going Eurocae ED-324. We propose a set of Machine Learning Requirements, a Machine Learning Model Description, and its implementation for a long short-term memory recurrent neural network. Finally, we verify the requirements on the implementation. Demonstrated on legacy avionics computers, the implementation is suitable for the deployment of the developed Machine Learning Model weight estimator on airborne targets for critical functions such as on-board alerting.

cs.LG↗

arXiv2026

Certified geometric robustness -- Super-DeepG

Safety-critical applications are required to perform as expected in normal operations. Image processing functions are often required to be insensitive to small geometric perturbations such as rotation, scaling, shearing or translation. This paper addresses the formal verification of neural networks against geometric perturbations on their image dataset. Our method Super-DeepG improves the reasoning used in linear relaxation techniques and Lipschitz optimization, and provides an implementation that leverages GPU hardware. By doing so, Super-DeepG achieves both precision and computational efficiency of robustness certification, to an extent that outperforms prior work. Super-DeepG is shared as an open-source tool on GitHub.

cs.AI↗

arXiv2026

Compilation and Execution of an Embeddable YOLO-NAS on the VTA

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 have explored the Versatile Tensor Accelerator (VTA) and showed its suitability for avionic applications. For that, we developed an initial stand-alone compiler designed with certification in mind. However, this compiler still suffers from some limitations that are overcome in this paper. The contributions consist in extending and fully automating the VTA compilation chain to allow complete CNN compilation and support larger CNNs (which parameters do not fit in the on-chip memory). The effectiveness is demonstrated by the successful compilation and simulated execution of a YOLO-NAS object detection model.

cs.AR↗

arXiv2026

Convolutions Predictable Offloading to an Accelerator: Formalization and Optimization

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 accelerators composed of an on-chip memory and a processing unit. However, the on-chip memory is often insufficient to store all the data required to compute a CNN layer. Thus, the computation must be performed in several offloading steps. We formalise such sequences of steps and apply our formalism to a state of the art decomposition of convolutions. In order to find optimal strategies in terms of duration, we encode the problem with a set of constraints. A Python-based simulator allows to analyse in-depth computed strategies.

cs.AR↗

arXiv2026

LARD 2.0: Enhanced Datasets and Benchmarking for Autonomous Landing Systems

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 object detection. Our main contributions include: (1) Enhancing dataset diversity, by advocating for the inclusion of new sources such as BingMap aerial images and Flight Simulator, to widen the generation scope of an existing dataset generator used to produce the dataset LARD; (2) Refining the Operational Design Domain (ODD), addressing issues like unrealistic landing scenarios and expanding coverage to multi-runway airports; (3) Benchmarking ML models for autonomous landing systems, introducing a framework for evaluating object detection subtask in a complex multi-instances setting, and providing associated open-source models as a baseline for AI models' performance.

cs.RO↗

arXiv2026

Extension of ACETONE C code generator for multi-core architectures

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 ACETONE. However, this framework is limited to generating sequential code, which cannot make most of the multi-core architectures. In this paper, we initiate an extension of ACETONE for the generation of parallel code by formally defining our processor assignment problem and surveying the state of the art on existing solutions. In the final paper, we will introduce the completed extension, including the implementation of the scheduling heuristic, the creation of templates implementing synchronization mechanisms, and an evaluation of the worst-case execution time of the framework's layers.

cs.DC↗

arXiv2026

Interferences within a certifiable design methodology for high-performance multi-core platforms

The adoption of high-performance multi-core platforms in avionics and automotive systems introduces significant challenges in ensuring predictable execution, primarily due to shared resource interferences. Many existing approaches study interference from a single angle-for example, through hardware-level analysis or by monitoring software execution. However, no single abstraction level is sufficient on its own. Hardware behavior, program structure, and system configuration all interact, and a complete view is needed to understand where interferences come from and how to reduce them. In this paper, we present a methodology that brings together several tools that operate at different abstraction levels. At the lowest level, PHYLOG provides a formal model of the hardware and identifies possible interference channels using micro-architectural transactions. At the program level, machine learning analysis locates the exact parts of the code that are most sensitive to shared-resource contention. At the compilation level, MLIR-based transformations use this information to reshape memory access patterns and reduce pressure on shared resources. Finally, at the system level, Linux cgroups enforce static execution constraints to prevent highly interfering tasks from running together. The goal of our approach is to reduce memory interference and improve the system's predictability, thereby easing the certification process of multi-core systems in safety-critical domains.

cs.DC↗

arXiv2025

VerifIoU -- Robustness of Object Detection to Perturbations

We introduce a novel Interval Bound Propagation (IBP) approach for the formal verification of object detection models, specifically targeting the Intersection over Union (IoU) metric. The approach has been implemented in an open source code, named IBP IoU, compatible with popular abstract interpretation based verification tools. The resulting verifier is evaluated on landing approach runway detection and handwritten digit recognition case studies. Comparisons against a baseline (Vanilla IBP IoU) highlight the superior performance of IBP IoU in ensuring accuracy and stability, contributing to more secure and robust machine learning applications.

cs.CV↗

arXiv2025

Open-source Stand-Alone Versatile Tensor Accelerator

Machine Learning (ML) applications demand significant computational resources, posing challenges for safety-critical domains like aeronautics. The Versatile Tensor Accelerator (VTA) is a promising FPGA-based solution, but its adoption was hindered by its dependency on the TVM compiler and by other code non-compliant with certification requirements. This paper presents an open-source, standalone Python compiler pipeline for the VTA, developed from scratch and designed with certification requirements, modularity, and extensibility in mind. The compiler's effectiveness is demonstrated by compiling and executing LeNet-5 Convolutional Neural Network (CNN) using the VTA simulators, and preliminary results indicate a strong potential for scaling its capabilities to larger CNN architectures. All contributions are publicly available.

cs.AR↗

arXiv2025

Implementation of airborne ML models with semantics preservation

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. Thus, their development will have to be demonstrated to be compliant with the adequate guidance. So far, the European Union Aviation Safety Agency (EASA) has published a concept paper and an EUROCAE/SAE group is preparing ED-324. Both approaches delineate high-level objectives to confirm the ML model achieves its intended function and maintains training performance in the target environment. The paper aims to clarify the difference between an ML model and its corresponding unambiguous description, referred to as the Machine Learning Model Description (MLMD). It then refines the essential notion of semantics preservation to ensure the accurate replication of the model. We apply our contributions to several industrial use cases to build and compare several target models.

cs.AI↗

arXiv2024

How to design a dataset compliant with an ML-based system ODD?

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-Learning (ML) system. Relying on emerging certification standards, we describe the process for establishing ODDs at both the system and image levels. In the process, we present the translation of high-level system constraints into actionable image-level properties, allowing for the definition of verifiable Data Quality Requirements (DQRs). To illustrate this approach, we use the Landing Approach Runway Detection (LARD) dataset which combines synthetic imagery and real footage, and we focus on the steps required to verify the DQRs. The replicable framework presented in this paper addresses the challenges of designing a dataset compliant with the stringent needs of ML-based systems certification in safety-critical applications.

cs.AI↗

arXiv2024

COOK Access Control on an embedded Volta GPU

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 inference tasks. These platforms, like the JETSON AGX XAVIER, embed several cores and accelerators in a SWaP- efficient (Size Weight and Power) package with a limited set of resources. However, concurrent applications tend to interfere on shared resources, resulting in high execution time variability for applications compared to their behaviour in isolation.Access control techniques aim to selectively restrict the flow of operations executed by a resource. To reduce the impact of interference on the JETSON Volta GPU, we specify and implement an access control technique to ensure each GPU operation executes in isolation to reduce its timing variability. We implement the controller using three different strategies and assess their complexity and impact on the application performance. Our evaluation shows the benefits of adding the access control: its transparency to applications, reduced timing variability, isolation between GPU operations, and small code complexity. However, the strategies may cause some potential slowdowns for applications even in isolation but which are reasonable.

cs.AR↗

arXiv2024

Formal description of ML models for unambiguous implementation

Implementing deep neural networks in safety critical systems, in particular in the aeronautical domain, will require to offer adequate specification paradigms to preserve the semantics of the trained model on the final hardware platform. We propose to extend the nnef language in order to allow traceable distribution and parallelisation optimizations of a trained model. We show how such a specification can be implemented in cuda on a Xavier platform.

cs.NE↗

arXiv2024

Towards the Certification of Hybrid Architectures: Analysing Interference on Hardware Accelerators through PML

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 cores and accelerators in a small package. However, in order to satisfy the Size, Weight and Power (SWaP) constraints, limited and shared resources are integrated. This paper presents an overview of the standards applicable to the certification of hybrid platforms and an early mapping of their objectives to said platforms. In particular, we consider how the classification of AMC20-152A for airborne electronic hardware applies to hybrid platforms. We also consider AMC20-193 for multi-core platforms, and how this standard fits different types of accelerators.

cs.AR↗

arXiv2024

Certified ML Object Detection for Surveillance Missions

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 objectives and provide sufficient evidences, required by the recommendations (soon to be published) of the ED 324 / ARP 6983 standard, to gain confidence in the dependability of the designed system.

cs.AI↗

arXiv2023

LARD -- Landing Approach Runway Detection -- Dataset for Vision Based Landing

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 commercial interest in autonomous landing systems in the aerospace field, there is a lack of open-source datasets of aerial images. To address this issue, we present a dataset-lard-of high-quality aerial images for the task of runway detection during approach and landing phases. Most of the dataset is composed of synthetic images but we also provide manually labelled images from real landing footages, to extend the detection task to a more realistic setting. In addition, we offer the generator which can produce such synthetic front-view images and enables automatic annotation of the runway corners through geometric transformations. This dataset paves the way for further research such as the analysis of dataset quality or the development of models to cope with the detection tasks. Find data, code and more up-to-date information at https://github.com/deel-ai/LARD

cs.CV↗

arXiv2021

White Paper Machine Learning in Certified Systems

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 vehicles, recognizing voice, etc. It is also an opportunity to implement and embed new capabilities out of the reach of classical implementation techniques. However, ML techniques introduce new potential risks. Therefore, they have only been applied in systems where their benefits are considered worth the increase of risk. In practice, ML techniques raise multiple challenges that could prevent their use in systems submitted to certification constraints. But what are the actual challenges? Can they be overcome by selecting appropriate ML techniques, or by adopting new engineering or certification practices? These are some of the questions addressed by the ML Certification 3 Workgroup (WG) set-up by the Institut de Recherche Technologique Saint Exupéry de Toulouse (IRT), as part of the DEEL Project.

cs.AI↗

arXiv2020

Safety Verification of Neural Network Controlled Systems

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 neural network based controller. We assume a generic model for the controller that can capture both simple and complex behaviours involving neural networks. Based on this model, we perform a reachability analysis that soundly approximates the reachable states of the overall system, allowing to achieve a formal proof of safety. To this end, we leverage both validated simulation to approximate the behaviour of the physical system and abstract interpretation to approximate the behaviour of the controller. We evaluate the applicability of our approach using a real-world use case. Moreover, we show that our approach can provide valuable information when the system cannot be proved totally safe.

cs.AI↗