Structured pruning and quantization are promising approaches for reducing the inference
time and memory footprint of neural networks. However, most existing methods require the …

Over the history of Artificial Neural Networks (ANNs), only a minority of algorithms integrate
structural changes of the network architecture into the learning process. Modern …

Resistive random-access memory (ReRAM)-based processing-in-memory (PIM)
architectures are used extensively to accelerate inferencing/training with convolutional …

The Vision Transformer architecture is a deep learning model inspired by the success of the
Transformer model in Natural Language Processing. However, the self-attention …

The Segment Anything Model (SAM) is a foundational model for image segmentation tasks,
known for its strong generalization across diverse applications. However, its impressive …

Despite many modern applications of Deep Neural Networks (DNNs), the large number of
parameters in the hidden layers makes them unattractive for deployment on devices with …

Sparsification technology is crucial for deploying convolutional neural networks in resource-
constrained environments. However, the efficiency of sparse models is hampered by …

Detecting persons in images or video with neural networks is a well-studied subject in
literature. However, such works usually assume the availability of a camera of decent …

This work delves into the exploration of optimizing Multilayer Perceptrons (MLP) or the
dense layers of other sorts of Deep Neural Networks when they are aimed at edge …

Les réseaux de neurones artificiels ont fondamentalement redéfini la façon dont les
données sont analysées et ouvert de nouvelles possibilités d'intelligence artificielle à travers …