Training deep learning models on mobile devices recently becomes possible, because of
increasing computation power on mobile hardware and the advantages of enhancing user …

Deep Neural Networks are allowing mobile devices to incorporate a wide range of features
into user applications. However, the computational complexity of these models makes it …

When building Deep Learning (DL) models, data scientists and software engineers manage
the trade-off between their accuracy, or any other suitable success criteria, and their …

Modern deep learning models have been exploited in various domains, including computer
vision (CV), natural language processing (NLP), search and recommendation. In practical AI …

Machine learning inference is increasingly being executed locally on mobile and embedded
platforms, due to the clear advantages in latency, privacy and connectivity. In this paper, we …

Recent years have witnessed an exponential increase in the use of mobile and embedded
devices. With the great success of deep learning in many fields, there is an emerging trend …

Recent years have witnessed an explosive growth of mobile devices. Mobile devices are
permeating every aspect of our daily lives. With the increasing usage of mobile devices and …

Deep neural networks (DNNs) have unleashed a new wave of applications on mobile
devices, such as various intelligent personal assistants. Most of these applications rely on …

Many Deep Neural Network (DNN) based applications have been developed and run on
mobile devices. Although these advanced DNN models can provide better results, they also …

Deploying deep learning (DL) models on mobile devices draws more and more attention
recently. However, designing an efficient inference engine on devices is under the great …