Influenced by the great success of deep learning via cloud computing and the rapid
development of edge chips, research in artificial intelligence (AI) has shifted to both of the …

With the vigorous development of artificial intelligence (AI), intelligence applications based
on deep neural networks (DNNs) have changed people's lifestyles and production …

With the proliferation of mobile computing and Internet of Things (IoT), massive mobile and
IoT devices are connected to the Internet. These devices are generating a huge amount of …

In many industry scale applications, large and resource consuming machine learning
models reside in powerful cloud servers. At the same time, large amounts of input data are …

The booming development of deep learning applications and services heavily relies on
large deep learning models and massive data in the cloud. However, cloud-based deep …

Nowadays, deep neural networks (DNNs) are the core enablers for many emerging edge AI
applications. Conventional approaches for training DNNs are generally implemented at …

This work is an introduction and a survey for the Special Issue on Machine Intelligence at the
Edge. The authors argue that workloads that were formerly performed in the cloud are …

Recent years have witnessed the widespread popularity of Internet of things (IoT). By
providing sufficient data for model training and inference, IoT has promoted the development …

Edge intelligence is emerging as a new interdiscipline to push learning intelligence from
remote centers to the edge of the network. However, with its widespread deployment, new …

As Multi-access Edge Computing (MEC) and 5G technologies evolve, new applications are
emerging with unprecedented capacity and real-time requirements. At the core of such …