Edge Intelligence (EI) integrates Edge Computing (EC) and Artificial Intelligence (AI) to push
the capabilities of AI to the network edge for real-time, efficient and secure intelligent …

Artificial intelligence (AI) technologies have emerged as pivotal enablers across a multitude
of industries, including consumer electronics, healthcare, and manufacturing, largely due to …

Edge intelligence has emerged as a promising paradigm to accelerate DNN inference by
model partitioning, which is particularly useful for intelligent scenarios that demand high …

This paper studies the integration of data collection and offloading for maritime Internet of
Things (IoT) systems with multiple unmanned aerial vehicles (UAVs). In the considered multi …

Recent years have witnessed the great success of vision transformer (ViT), which has
achieved state-of-the-art performance on multiple computer vision benchmarks. However …

Model partitioning is a promising technique for improving the efficiency of distributed
inference by executing partial deep neural network (DNN) models on edge servers (ESs) or …

Embedded distributed inference of Neural Networks has emerged as a promising approach
for deploying machine-learning models on resource-constrained devices in an efficient and …

With promising benefits of splitting deep neural network (DNN) computation loads to the
edge server, split computing has been a novel paradigm achieving high-quality artificial …

Artificial intelligence (AI) technologies have emerged as pivotal enablers across a multitude
of industries, including consumer electronics, healthcare, and manufacturing, largely due to …

Deep learning technology has grown significantly in new application scenarios such as
smart cities and driverless vehicles, but its deployment needs to consume a lot of resources …