Deep neural networks (DNNs) are successful in many computer vision tasks. However, the
most accurate DNNs require millions of parameters and operations, making them energy …

Embedding Artificial Intelligence onto low-power devices is a challenging task that has been
partly overcome with recent advances in machine learning and hardware design. Presently …

Channel pruning is an important method to speed up CNN model's inference. Previous filter
pruning algorithms regard importance evaluation and model fine-tuning as two independent …

Graph neural networks (GNNs) are information processing architectures for signals
supported on graphs. They are presented here as generalizations of convolutional neural …

Video action recognition is a complex task dependent on modeling spatial and temporal
context. Standard approaches rely on 2D or 3D convolutions to process such context …

Recent advances in artificial intelligence (AI) on the Internet of Things (IoT) devices have
realized edge AI in several applications by enabling low latency and energy efficiency …

In most cases deep learning architectures are trained disregarding the amount of operations
and energy consumption. However, some applications, like embedded systems, can be …

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 …

Vision transformers have been successfully applied to image recognition tasks due to their
ability to capture long-range dependencies within an image. However, they still suffer from …

Deep Neural Networks (DNNs) in general and Convolutional Neural Networks (CNNs) in
particular are state-of-the-art in numerous computer vision tasks such as object classification …