The success of deep neural networks (DNNs) is heavily dependent on computational
resources. While DNNs are often employed on cloud servers, there is a growing need to …

With the rapid development of Internet-of-Things (IoT) and the explosive advance of deep
learning, there is an urgent need to enable deep learning inference on IoT devices in Mobile …

Mobile devices can generate a tremendous amount of unique data, and thus, create
countless opportunities for deep learning tasks. Due to the concerns of data privacy, it is …

Recently, deploying deep neural network (DNN) models via collaborative inference, which
splits a pre-trained model into two parts and executes them on user equipment (UE) and …

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 …

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

Deep neural network (DNN)-enabled edge intelligence has been widely adopted to support
a variety of smart applications because of its ability to preserve privacy and conserve …

Deep Neural Network (DNN) based artificial intelligence help driving the great development
of mobile Internet. However, the hardware of a mobile device may not be sufficiently to meet …

As an emerging computing paradigm, edge computing can assist user equipments (UEs) in
executing computation-intensive deep neural network (DNN) inference tasks, thereby …

Recent advances in artificial intelligence have driven increasing intelligent applications at
the network edge, such as smart home, smart factory, and smart city. To deploy …