28 citations · 37 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
Efficient Hardware Implementation of Incremental Learning and Inference on Chip
Ghouthi Boukli Hacene, Vincent Gripon, Nicolas Farrugia +2
In this paper, we tackle the problem of incrementally learning a classifier, one example at a time, directly on chip. To this end, we propose an efficient hardware implementation o…
Quantized Guided Pruning for Efficient Hardware Implementations of Convolutional Neural Networks
Ghouthi Boukli Hacene, Vincent Gripon, Matthieu Arzel +2
Convolutional Neural Networks (CNNs) are state-of-the-art in numerous computer vision tasks such as object classification and detection. However, the large amount of parameters the…
Transfer Incremental Learning using Data Augmentation
Ghouthi Boukli Hacene, Vincent Gripon, Nicolas Farrugia +2
Deep learning-based methods have reached state of the art performances, relying on large quantity of available data and computational power. Such methods still remain highly inappr…