Mathieu Léonardon

Mathieu Léonardon
Bordeaux INP

PhD

About

18
Publications
4,904
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94
Citations

Publications

Publications (18)
Preprint
Full-text available
Deep neural networks are the state of the art in many computer vision tasks. Their deployment in the context of autonomous vehicles is of particular interest, since their limitations in terms of energy consumption prohibit the use of very large networks, that typically reach the best performance. A common method to reduce the complexity of these ar...
Preprint
Full-text available
Structured pruning is a popular method to reduce the cost of convolutional neural networks, that are the state of the art in many computer vision tasks. However, depending on the architecture, pruning introduces dimensional discrepancies which prevent the actual reduction of pruned networks. To tackle this problem, we propose a method that is able...
Article
Convolutional neural networks (CNN) have proven very effective in a variety of practical applications involving Artificial Intelligence (AI). However, the layer depth of CNN deepens as user applications become more sophisticated, resulting in a huge number of operations and increased memory size. The massive amount of the produced intermediate data...
Article
Full-text available
Introduced in the late 1980s for generalization purposes, pruning has now become a staple for compressing deep neural networks. Despite many innovations in recent decades, pruning approaches still face core issues that hinder their performance or scalability. Drawing inspiration from early work in the field, and especially the use of weight decay t...
Preprint
Full-text available
Polar codes can theoretically achieve very competitive Frame Error Rates. In practice, their performance may depend on the chosen decoding procedure, as well as other parameters of the communication system they are deployed upon. As a consequence, designing efficient polar codes for a specific context can quickly become challenging. In this paper,...
Preprint
Full-text available
Introduced in the late 80's for generalization purposes, pruning has now become a staple to compress deep neural networks. Despite many innovations brought in the last decades, pruning approaches still face core issues that hinder their performance or scalability. Drawing inspiration from early work in the field, and especially the use of weight-de...
Article
Full-text available
Flexibility is one mandatory aspect of channel coding in modern wireless communication systems. Among other things, the channel decoder has to support several code lengths and code rates. This need for flexibility applies to polar codes that are considered for control channels in the future 5G standard. This paper presents a new generic and flexibl...
Article
Full-text available
AFF3CT is an open source toolbox dedicated to Forward Error Correction (FEC or channel coding). It supports a broad range of codes: from widespread turbo codes and Low-Density Parity-Check (LDPC) codes to more recent polar codes. The toolbox is written in C++ and can be used either as a simulator to quickly evaluate algorithms characteristics, or a...
Poster
Full-text available
Dans cet article nous présentons un environne-ment de simulation de Monte Carlo pour les systèmes de communications numériques. Nous nous focalisons en particulier sur les fonctions associées au codage de canal. Après avoir présenté les enjeux liés à la simulation , nous identifions trois problèmes inhérents à ce type de simulation. Puis nous prése...
Article
Full-text available
The recent evolution of mobile communication systems toward a 5G network is associated with the search for new types of non-orthogonal modulations such as Sparse Code Multiple Access (SCMA). Such modulations are proposed in response to demands for increasing the number of connected users. SCMA is a non-orthogonal multiple access technique that offe...
Thesis
Full-text available
Les codes polaires constituent une classe de codes correcteurs d’erreurs inventés récemment qui suscite l’intérêt des chercheurs et des industriels, comme en atteste leur sélection pour le codage des canaux de contrôle dans la prochaine génération de téléphonie mobile (5G). Un des enjeux des futurs réseaux mobiles est la virtualisation des traiteme...
Conference Paper
Full-text available
Cloud Radio Access Network is foreseen as one of the key features of the future 5G mobile communication standard. In this context, all the baseband processing is intended to be performed on CPUs in order to keep a high level of flexibility. The challenge is then to propose efficient software implementation of baseband processing algorithms that gua...
Poster
Full-text available
This demonstration intends to present AFF3CT (A Fast Forward 3rror Correction Tool). The main objective of AFF3CT is to provide a portable, open source, fast and flexible software to the channel coding community in such a way that researchers can spend more time on channel coding / algorithmic problems instead of software development issues. It is...

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Projects

Project (1)
Project
AFF3CT is a project dedicated to the Forward Error Correction (FEC or channel coding). It is written in C++11 and it supports a large range of codes: from the well-spread Turbo codes to the very new Polar codes including the Low-Density Parity-Check (LDPC) codes. AFF3CT can be used in two different ways, as: - a dedicated toolbox or library for your projects, many modules (like codecs, modems, channels, ...) are available and easy to use, - a standalone application for Monte Carlo BER/FER and EXIT chart simulations. GitHub: https://github.com/aff3ct/aff3ct Web site: http://aff3ct.github.io