Deep Neural Network (DNN) splitting is one of the key enablers of edge Artificial Intelligence
(AI), as it allows end users to pre-process data and offload part of the computational burden …

Learning and inference at the edge is all about distilling, exchanging, and processing data
in a cooperative and distributed way, to achieve challenging trade-offs involving energy …

Split inference facilitates deep neural network (DNN) inference tasks at resource-
constrained edge devices. However, a pre-determined split configuration of a DNN limits the …

As a key technology of enabling Artificial Intelligence (AI) applications in 5G era, Deep
Neural Networks (DNNs) have quickly attracted widespread attention. However, it is …

Edge machine learning can deliver low-latency and private artificial intelligent (AI) services
for mobile devices by leveraging computation and storage resources at the network edge …

Edge intelligence is expected to be a key enabler of the future sixth generation (6G) mobile
network. However, the heterogeneous characteristics of edge intelligence, such as …

Deep neural networks (DNNs) are state-of-the-art solutions for many machine learning
applications, and have been widely used on mobile devices. Running DNNs on …

As the complexity of Deep Neural Network (DNN) models increases, their deployment on
mobile devices becomes increasingly challenging, especially in complex vision tasks such …

Edge machine learning involves the development of learning algorithms at the network edge
to leverage massive distributed data and computation resources. Among others, the …

Deploying deep neural networks (DNNs) on IoT and mobile devices is a challenging task
due to their limited computational resources. Thus, demanding tasks are often entirely …