Search Results for author: Mathieu Léonardon

Found 5 papers, 4 papers with code

DeepGEMM: Accelerated Ultra Low-Precision Inference on CPU Architectures using Lookup Tables

no code implementations18 Apr 2023 Darshan C. Ganji, Saad Ashfaq, Ehsan Saboori, Sudhakar Sah, Saptarshi Mitra, MohammadHossein AskariHemmat, Alexander Hoffman, Ahmed Hassanien, Mathieu Léonardon

A lot of recent progress has been made in ultra low-bit quantization, promising significant improvements in latency, memory footprint and energy consumption on edge devices.

Quantization

Leveraging Structured Pruning of Convolutional Neural Networks

1 code implementation13 Jun 2022 Hugo Tessier, Vincent Gripon, Mathieu Léonardon, Matthieu Arzel, David Bertrand, Thomas Hannagan

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.

Using Deep Neural Networks to Predict and Improve the Performance of Polar Codes

1 code implementation11 May 2021 Mathieu Léonardon, Vincent Gripon

Polar codes can theoretically achieve very competitive Frame Error Rates.

Rethinking Weight Decay For Efficient Neural Network Pruning

1 code implementation20 Nov 2020 Hugo Tessier, Vincent Gripon, Mathieu Léonardon, Matthieu Arzel, Thomas Hannagan, David Bertrand

Introduced in the late 1980s for generalization purposes, pruning has now become a staple for compressing deep neural networks.

Efficient Neural Network Network Pruning

Cannot find the paper you are looking for? You can Submit a new open access paper.