Mathieu Leonardon | Mathieu Léonardon

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Analyzing Few-Shot Neural Architecture Search in a Metric-Driven Framework
FLoCoRA: Federated Learning Compression With Low-Rank Adaptation
Design Environment of Quantization-Aware Edge AI Hardware for Few-Shot Learning
PEFSL: A deployment Pipeline for Embedded Few-Shot Learning on a FPGA SoC
Federated learning compression designed for lightweight communications
Federated learning compression designed for lightweight communications
Pipelined Architecture for a Semantic Segmentation Neural Network on FPGA
Pipelined Architecture for a Semantic Segmentation Neural Network on FPGA
Compression de réseaux de neurones pour l'apprentissage fédéré
Co-conception d'un réseau de neurones convolutif de segmentation sémantique et de son architecture matérielle
DeepGEMM: Accelerated Ultra Low-Precision Inference on CPU Architectures using Lookup Tables
Leveraging Structured Pruning of Convolutional Neural Networks
Energy Consumption Analysis of pruned Semantic Segmentation Networks on an Embedded GPU
Inter-Operability of Compression Techniques for Efficient Deployment of CNNs on Microcontrollers
Inter-Operability of Compression Techniques for Efficient Deployment of CNNs on Microcontrollers
Élagage de réseaux profond de neurones par dégradation sélective des pondérations
Investigating the Not-So-Obvious Effects of Structured Pruning
MOL-based In-Memory Computing of Binary Neural Networks
Rethinking Weight Decay for Efficient Neural Network Pruning
A Flexible and Portable Real-time DVB-S2 Transceiver using Multicore and SIMD CPUs
Using Deep Neural Networks to Predict and Improve the Performance of Polar Codes
AFF3CT: A Fast Forward Error Correction Toolbox!
Fast and Flexible Software Polar List Decoders
Toward High-Performance Implementation of 5G SCMA Algorithms
Décodage de codes polaires sur des architectures programmables
Transport Triggered Polar Decoders
Custom Low Power Processor for Polar Decoding
Fast Simulation and Prototyping with AFF3CT
Improving performance of SCMA MPA decoders using estimation of conditional probabilities