… of blending deep learning and mobile devices is just the beginning. … deep learning on mobile
devices from three aspects: training by using mobile data, inference on mobile devices, and …

… 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, …

… that using mobile devices to support deep learning is feasible, … deep learning on mobile
devices. The contributions in this study are as follows: (1) extend commonly used deep learning …

… front-end devices with more powerful back-end “helpers” that allow deep learning to be …
system parameters of video resolution, deep learning model size, and offloading decision. …

… 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 …

… The blending of learning algorithms and mobile computing taking place today is only the …
particular, that deep learning will play a prominent role in the evolution of smart devices (such …

… , traditional machine learning technologies. … mobile devices has attracted significant attention.
Thanks to this technology, portable devices may become smart objects capable of learning …

… 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 …

… 2,000 images per second on mobile devices. Software: Beyond these hardware advances,
there are also software platforms that seek to optimize deep learning on mobile devices (eg, […

… This diversity is relatively recent for mobile devices and we propose a layer-wise partitioning
approach followed by solving an optimization equation (see §VA) to decide how this …