Mathieu Léonardon

Associate Professor in Electronics

IMT Atlantique


I conduct research at the IMT Atlantique in Brest on hardware and software implementations of signal processing and AI algorithms. I teach computer engineering and digital electronics.

My PhD thesis focused on the implementation of polar codes decoders. I proposed the fastest software implementation of the Adaptive SC List decoding algorithm to date. This implementation is integrated in the AFF3CT toolbox to which I actively contribute. I’m also part of the organisation committee of the next ISTC 2023 conference.

I currently focus on efficient hardware and software implementations of Neural Networks, aiming at low latency and energy efficiency, through multiple industrial collaborations, and in the near future as the coordinator of a JCJC ANR project, ProPruNN.


  • Channel Coding
  • Embedded Electronics
  • HPC
  • AI


  • PhD in Electronics, 2018

    Polytechnique Montréal

  • PhD in Electronics, 2018

    University of Bordeaux

  • MEng in Embedded Electronics, 2015

    Enseirb-Matmeca, Bordeaux INP


A Fast Forward Error Correction Toolbox

Simulate high-throughput communication chains.


Source code on GitHub Website

Latest release

Recent Publications

Leveraging Structured Pruning of Convolutional Neural Networks

Structured pruning is a popular method to reduce the cost of convolutional neural networks, that are the state of the art in many …

Energy Consumption Analysis of pruned Semantic Segmentation Networks on an Embedded GPU

Deep neural networks are the state of the art in many computer vision tasks. Their deployment in the context of autonomous vehicles is …

Inter-Operability of Compression Techniques for Efficient Deployment of CNNs on Microcontrollers

Machine Learning (ML) has become state of the art for various tasks, including classification of accelerometer data. In the world of …

Élagage de réseaux profond de neurones par dégradation sélective des pondérations

Les réseaux de neurones profonds sont le standard incontournable de l’apprentissage automatique. Cependant, pour atteindre les …

Investigating the Not-So-Obvious Effects of Structured Pruning

Structured pruning is a popular method to reduce the cost of convolutional neural networks. However, depending on the architecture, …


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