As the computing paradigm shifts from cloud computing to end-edge-cloud computing, it
also supports artificial intelligence evolving from a centralized manner to a distributed one …

In recent years, the landscape of computing paradigms has witnessed a gradual yet
remarkable shift from monolithic computing to distributed and decentralized paradigms such …

Edge devices can offload deep neural network (DNN) inference to the cloud to overcome
energy or processing constraints. Nevertheless, offloading adds communication delay …

Deep neural networks (DNNs) typically have a single exit point that makes predictions by
running the entire stack of neural layers. Since not all inputs require the same amount of …

Deep learning is nowadays considered state-of-the-art technology in many applications
thanks to huge performance capabilities. However, the accuracy levels that can be obtained …

Today, artificial neural networks are the state of the art for solving a variety of complex tasks,
especially in image classification. Such architectures consist of a sequence of stacked layers …

Transforming off-the-shelf deep neural network (DNN) models into dynamic multi-exit
architectures can achieve inference and transmission efficiency by fragmenting and …

Deploying Machine learning (ML) on milliwatt-scale edge devices (tinyML) is gaining
popularity due to recent breakthroughs in ML and Internet of Things (IoT). Most tinyML …

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 …

Pre-trained models (PTMs) have demonstrated great success in a variety of NLP and CV
tasks and have become a significant development in the field of deep learning. However, the …