  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 intelligent applications, there is a soaring demand
for mobile applications with machine learning services. Inspired by the
tremendous success achieved by deep learning in many machine learning tasks, it
becomes a natural trend to push deep learning towards mobile applications.
However, there exist many challenges to realize deep learning in mobile
applications, including the contradiction between the miniature nature of
mobile devices and the resource requirement of deep neural networks, the
privacy and security concerns about individuals' data, and so on. To resolve
these challenges, during the past few years, great leaps have been made in this
area. In this paper, we provide an overview of the current challenges and
representative achievements about pushing deep learning on mobile devices from
three aspects: training with mobile data, efficient inference on mobile
devices, and applications of mobile deep learning. The former two aspects cover
the primary tasks of deep learning. Then, we go through our two recent
applications that apply the data collected by mobile devices to inferring mood
disturbance and user identification. Finally, we conclude this paper with the
discussion of the future of this area.
