Multi-Access Edge Computing (MEC) attracts much attention from the scientific community
due to its scientific, technical, and commercial implications. In particular, the European …

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

Computer networks are dealing with growing complexity, given the ever-increasing volume
of data produced by all sorts of network nodes. Performance improvements are a non-stop …

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 network (DNN) partition is a research problem that involves splitting a DNN into
multiple parts and offloading them to specific locations. Because of the recent advancement …

Edge offloading for deep neural networks (DNNs) can be adaptive to the input's complexity
by using early-exit DNNs. These DNNs have side branches throughout their architecture …

In computer vision applications, mobile devices can transfer the inference of Convolutional
Neural Networks (CNNs) to the cloud due to their computational restrictions. Nevertheless …

The cloud-edge collaborative inference demands scheduling the artificial intelligence (AI)
tasks efficiently to the appropriate edge smart device. However, the continuously iterative …

The cloud–edge collaborative inference requires efficient scheduling of artificial intelligence
(AI) tasks to the appropriate edge intelligence devices. Gls DNN inference latency has …