  In the last five years, edge computing has attracted tremendous attention
from industry and academia due to its promise to reduce latency, save
bandwidth, improve availability, and protect data privacy to keep data secure.
At the same time, we have witnessed the proliferation of AI algorithms and
models which accelerate the successful deployment of intelligence mainly in
cloud services. These two trends, combined together, have created a new
horizon: Edge Intelligence (EI). The development of EI requires much attention
from both the computer systems research community and the AI community to meet
these demands. However, existing computing techniques used in the cloud are not
applicable to edge computing directly due to the diversity of computing sources
and the distribution of data sources. We envision that there missing a
framework that can be rapidly deployed on edge and enable edge AI capabilities.
To address this challenge, in this paper we first present the definition and a
systematic review of EI. Then, we introduce an Open Framework for Edge
Intelligence (OpenEI), which is a lightweight software platform to equip edges
with intelligent processing and data sharing capability. We analyze four
fundamental EI techniques which are used to build OpenEI and identify several
open problems based on potential research directions. Finally, four typical
application scenarios enabled by OpenEI are presented.
