… report by Cisco Systems [1], half a billion mobile devices were globally sold in 2015, and
the mobile data traffic grew by 74 percent generating 3.7 exabytes (1 exabyte = 1018 bytes) of …

… critical to improve the performance of increasingly heavy deep learning (DL) models. However,
… framework for the CPU and GPU on mobile devices. It can fully utilize the heterogeneous …

… deep learning methods can help glaucoma screening by extending it to larger populations
using retinal images. Low-cost lenses attached to mobile devices … and mobile devices. The …

… Different from the cloud and mobile devices, microcontrollers are bare-metal devices that do
not have an operating system. Therefore, we need to jointly design the deep learning model …

… presents a deep learning approach for depressive episode detection on mobile devices, …
To run DL4DED on mobile devices, two neural network model compression techniques are …

… In this paper, we propose and explore an alternative approach, a DeepSense framework, to
execute deep learning algorithms on mobile devices without cloud offloading. By leveraging …

… deep learning optimized system which contains a cloud server and multiple smartphone
devices … Considering the personal mobile devices such as smartphones are becoming …

… Abstract—This paper examines the application of a deep learning approach to automatic
coin recognition, via a mobile device and client-server architecture. We show that a con…

… mobile device support for deep learn… deep learning model architectures (viz. CNNs –
Convolutional Neural Networks, and DNNs – Deep Neural Networks) as used in four existing deep …

… activities, given different deep learning models and inference … deep learning inference, in
this paper we develop a mobile … on adapting deep neural networks to the mobile devices, we …