The computational workload involved in Convolutional Neural Networks (CNNs) is typically
out of reach for low-power embedded devices. The scientific literature provides a large …

Deep learning and especially the use of Deep Neural Networks (DNNs) provide impressive
results in various regression and classification tasks. However, to achieve these results …

Inference time, model size, and accuracy are critical for deploying deep neural network
models. Numerous research efforts have been made to compress neural network models …

In recent years, deep learning models have achieved remarkable successes in various
applications, such as pattern recognition, computer vision, and signal processing. However …

Embedding second-order operations into deep convolutional neural networks (CNNs) has
recently shown impressive performance for a number of vision tasks. Specifically, the two …

Most of the existing works use projection functions for ternary quantization in discrete space.
Scaling factors and thresholds are used in some cases to improve the model accuracy …

Deep learning and especially the use of Deep Neural Networks (DNNs) provide impressive
results in various regression and classification tasks. However, to achieve these results …

The outstanding performance achieved by Convolutional Neural Network (CNN) comes at
the cost of extremely high computational requirements, making them out of reach for most …

Les performances exceptionnelles atteintes par les Convolutional Neural Network (CNN) se
font au prix d'exigences de calcul extrêmement élevées, les rendant hors de portée de la …