  Recent breakthroughs in deep learning and artificial intelligence
technologies have enabled numerous mobile applications. While traditional
computation paradigms rely on mobile sensing and cloud computing, deep learning
implemented on mobile devices provides several advantages. These advantages
include low communication bandwidth, small cloud computing resource cost, quick
response time, and improved data privacy. Research and development of deep
learning on mobile and embedded devices has recently attracted much attention.
This paper provides a timely review of this fast-paced field to give the
researcher, engineer, practitioner, and graduate student a quick grasp on the
recent advancements of deep learning on mobile devices. In this paper, we
discuss hardware architectures for mobile deep learning, including Field
Programmable Gate Arrays, Application Specific Integrated Circuit, and recent
mobile Graphic Processing Units. We present Size, Weight, Area and Power
considerations and their relation to algorithm optimizations, such as
quantization, pruning, compression, and approximations that simplify
computation while retaining performance accuracy. We cover existing systems and
give a state-of-the-industry review of TensorFlow, MXNet, Mobile AI Compute
Engine, and Paddle-mobile deep learning platform. We discuss resources for
mobile deep learning practitioners, including tools, libraries, models, and
performance benchmarks. We present applications of various mobile sensing
modalities to industries, ranging from robotics, healthcare and multi-media,
biometrics to autonomous drive and defense. We address the key deep learning
challenges to overcome, including low quality data, and small
training/adaptation data sets. In addition, the review provides numerous
citations and links to existing code bases implementing various technologies.
