Network compression techniques that combine tensor decompositions and pruning have
shown promise in leveraging the advantages of both strategies. In this work, we propose …

Abstract In recent years, Artificial Intelligence (AI) and Machine learning (ML) have gained
significant interest from both, industry and academia. Notably, conventional ML techniques …

With the increasing stringent QoS constraints (eg, latency, bandwidth, jitter) imposed by
novel applications (eg, e-Health, autonomous vehicles, smart cities, etc.), as well as the …

Abstract Deep Neural Networks (DNNs) enjoy the welfare of convolution, while also bearing
huge computational pressure. Therefore, model compression techniques are used to …

The achievement of convolutional neural networks (CNNs) in a variety of applications is
accompanied by a dramatic increase in computational costs and memory requirements. In …

We present SPSRC, a novel, simple and effective framework to extract enhanced Structured
Pruning Saliency scores by Reorganizing Convolution. We observe that performance of …

In this work, the network complexity should be reduced with a concomitant reduction in the
number of necessary training examples. The focus thus was on the dependence of proper …

Facing enormous data generated at the network edge, Edge Intelligence (EI) emerges as
the fusion of Edge Computing and Artificial Intelligence, revolutionizing edge data …

Cloud computing is a critical component in the success of 5G and 6G networks, particularly
given the computation-intensive nature of emerging applications. Despite all it advantages …

Convolutional neural networks (CNNs) have shown state-of-the-art performance in various
applications. However, CNNs are resource-hungry due to their requirement of high …