… This article provides an overview of efficient deep learning … We then cover efficient on-device
training to enable user … , we introduce the efficient deep learning system design from …

… mobile devices. Specifically, we summarize and compare the state-of-the-art DL techniques
on mobile devices … We generalize an optimization pipeline for bringing DL to mobile devices, …

… Besides, the excessive energy consumption from performing deep learning models on
battery-powered mobile devices is also a severe problem to be solved. Moreover, diverse …

… device-end solution to protect mobile devices from malware threats in real-time efficiently
by leveraging customized deep … to detect malware directly on mobile devices as a pre-installed …

… deep learning-based indoor localization framework that is expected to deliver accurate results
on various mobile devices … varying deep learning model depths and across diverse mobile …

… , this study proposes a novel deep learning network C-DenseNet which … deep learning
based wheat strip rust disease grading algorithms by using color images taken by mobile devices…

… DNN inference on mobile devices aiming for both real-time performance and high accuracy.
The difficulty of achieving real-time DNN inference on mobile devices necessitates the …

… are intelligent sensor systems and IoTs, deep learning in mobile devices, deep learning
training using general-purpose GPUs, deep learning in heterogeneous computing systems, …

… 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 …

… a mobile application for real-time image classifications because of low specifications, limited
hardware resources (memory and power) and the computing ability of mobile devices. To …