With the deep penetration of mobile devices, more and more mobile deep learning
applications have been widely used in daily life. However, since deep learning tasks are …

Cognitive services have revolutionized the way we live, work and interact with the world. In
recent years, deep neural networks have become the mainstream approach in cognitive …

The surging Deep Neural Network (DNN) based applications are becoming increasingly
popular in mobile computing. However, they impose significant challenges for mobile …

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 …

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

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 …

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

Recent breakthroughs in deep learning (DL) have led to the emergence of many intelligent
mobile applications and services, but in the meanwhile also pose unprecedented computing …

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

The quality of service (QoS) of intelligent applications on mobile devices heavily depends on
the inference speed of Deep Neural Network (DNN) models. Cooperative DNN inference …