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20092023
most citedQuantization and Deployment of Deep Neural Networks on Microcontrollers

207 citations · 516 across the 63 of their papers we have counts for

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11 papers · 1 filter

cs.NE2022★ 1 cited

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

Hugo Tessier, Vincent Gripon, Mathieu Léonardon +3

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 limitatio…

cs.NE2022★ 2 cited

Leveraging Structured Pruning of Convolutional Neural Networks

Hugo Tessier, Vincent Gripon, Mathieu Léonardon +3

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 a…

cs.NE2020★ 28 cited

Rethinking Weight Decay For Efficient Neural Network Pruning

Hugo Tessier, Vincent Gripon, Mathieu Léonardon +3

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…

cs.NE2020★ 6 cited

GPU-based Self-Organizing Maps for Post-Labeled Few-Shot Unsupervised Learning

Lyes Khacef, Vincent Gripon, Benoit Miramond

Few-shot classification is a challenge in machine learning where the goal is to train a classifier using a very limited number of labeled examples. This scenario is likely to occur…

cs.NE2019★ 2 cited

Attention Based Pruning for Shift Networks

Ghouthi Boukli Hacene, Carlos Lassance, Vincent Gripon +2

In many application domains such as computer vision, Convolutional Layers (CLs) are key to the accuracy of deep learning methods. However, it is often required to assemble a large…

cs.NE2017

Robust Associative Memories Naturally Occuring From Recurrent Hebbian Networks Under Noise

Eliott Coyac, Vincent Gripon, Charlotte Langlais +1

The brain is a noisy system subject to energy constraints. These facts are rarely taken into account when modelling artificial neural networks. In this paper, we are interested in…