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Deep neural network (DNN) partition is a research problem that involvessplitting a DNN into multiple parts and offloading them to specific locations.Because of the recent advancement in multi-access edge computing and edgeintelligence, DNN partition has been considered as a powerful tool forimproving DNN inference performance when the computing resources of edge andend devices are limited and the remote transmission of data from these devicesto clouds is costly. This paper provides a comprehensive survey on the recentadvances and challenges in DNN partition approaches over the cloud, edge, andend devices based on a detailed literature collection. We review how DNNpartition works in various application scenarios, and provide a unifiedmathematical model of the DNN partition problem. We developed afive-dimensional classification framework for DNN partition approaches,consisting of deployment locations, partition granularity, partitionconstraints, optimization objectives, and optimization algorithms. Eachexisting DNN partition approache can be perfectly defined in this framework byinstantiating each dimension into specific values. In addition, we suggest aset of metrics for comparing and evaluating the DNN partition approaches. Basedon this, we identify and discuss research challenges that have not yet beeninvestigated or fully addressed. We hope that this work helps DNN partitionresearchers by highlighting significant future research directions in thisdomain.

A Survey on Deep Neural Network Partition over Cloud, Edge and End Devices