As the backbone technology of machine learning, deep neural networks (DNNs) have have
quickly ascended to the spotlight. Running DNNs on resource-constrained mobile devices …

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 …

The recent breakthrough in artificial intelligence (AI), especially deep neural networks
(DNNs), has affected every branch of science and technology. Particularly, edge AI has …

Abstract Deep Neural Networks (DNNs) based on intelligent applications have been
intensively deployed on mobile devices. Unfortunately, resource-constrained mobile devices …

Ubiquitous sensors and smart devices from factories and communities are generating
massive amounts of data, and ever-increasing computing power is driving the core of …

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 …

Edge intelligence, as a prospective paradigm for accelerating DNN inference, is mostly
implemented by model partitioning which inevitably incurs the large transmission overhead …

Mobile Edge Computing (MEC) has emerged as a promising paradigm catering to
overwhelming explosions of mobile applications, by offloading compute-intensive tasks to …

AI applications powered by deep learning inference are increasingly run natively on edge
devices to provide better interactive user experience. This often necessitates fitting a model …

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