  Edge computing and artificial intelligence (AI), especially deep learning for
nowadays, are gradually intersecting to build a novel system, called edge
intelligence. However, the development of edge intelligence systems encounters
some challenges, and one of these challenges is the \textit{computational gap}
between computation-intensive deep learning algorithms and less-capable edge
systems. Due to the computational gap, many edge intelligence systems cannot
meet the expected performance requirements. To bridge the gap, a plethora of
deep learning techniques and optimization methods are proposed in the past
years: light-weight deep learning models, network compression, and efficient
neural architecture search. Although some reviews or surveys have partially
covered this large body of literature, we lack a systematic and comprehensive
review to discuss all aspects of these deep learning techniques which are
critical for edge intelligence implementation. As various and diverse methods
which are applicable to edge systems are proposed intensively, a holistic
review would enable edge computing engineers and community to know the
state-of-the-art deep learning techniques which are instrumental for edge
intelligence and to facilitate the development of edge intelligence systems.
This paper surveys the representative and latest deep learning techniques that
are useful for edge intelligence systems, including hand-crafted models, model
compression, hardware-aware neural architecture search and adaptive deep
learning models. Finally, based on observations and simple experiments we
conducted, we discuss some future directions.
