While high accuracy is of paramount importance for deep learning (DL) inference, serving
inference requests on time is equally critical but has not been carefully studied especially …

In recent years, the rapid development of edge computing enables us to process a wide
variety of intelligent applications at the edge, such as real-time video analytics. However …

An increasing number of applications rely on complex inference tasks that are based on
machine learning (ML). Currently, there are two options to run such tasks: either they are …

To meet increasing demands for machine-learning-based applications, pushing inference
services to the network edge has been a trend. This work aims to design an on-demand …

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

By deploying machine learning algorithms at the network edge, edge learning recently
emerges as a promising framework to support intelligent mobile services. It effectively …

Edge Learning (EL) pushes the computational resources toward the edge of 5G/6G network
to assist mobile users requesting delay-sensitive and energy-aware intelligent services. A …

Edge intelligence (EI) becomes a trend to push the deep learning frontiers to the network
edge, so that deep neural networks (DNNs) applications can be well leveraged at resource …

By deploying machine-learning algorithms at the network edge, edge learning can leverage
the enormous real-time data generated by billions of mobile devices to train AI models …