  Deep neural network (DNN) partition is a research problem that involves
splitting a DNN into multiple parts and offloading them to specific locations.
Because of the recent advancement in multi-access edge computing and edge
intelligence, DNN partition has been considered as a powerful tool for
improving DNN inference performance when the computing resources of edge and
end devices are limited and the remote transmission of data from these devices
to clouds is costly. This paper provides a comprehensive survey on the recent
advances and challenges in DNN partition approaches over the cloud, edge, and
end devices based on a detailed literature collection. We review how DNN
partition works in various application scenarios, and provide a unified
mathematical model of the DNN partition problem. We developed a
five-dimensional classification framework for DNN partition approaches,
consisting of deployment locations, partition granularity, partition
constraints, optimization objectives, and optimization algorithms. Each
existing DNN partition approache can be perfectly defined in this framework by
instantiating each dimension into specific values. In addition, we suggest a
set of metrics for comparing and evaluating the DNN partition approaches. Based
on this, we identify and discuss research challenges that have not yet been
investigated or fully addressed. We hope that this work helps DNN partition
researchers by highlighting significant future research directions in this
domain.
