  In many industry scale applications, large and resource consuming machine
learning models reside in powerful cloud servers. At the same time, large
amounts of input data are collected at the edge of cloud. The inference results
are also communicated to users or passed to downstream tasks at the edge. The
edge often consists of a large number of low-power devices. It is a big
challenge to design industry products to support sophisticated deep model
deployment and conduct model inference in an efficient manner so that the model
accuracy remains high and the end-to-end latency is kept low. This paper
describes the techniques and engineering practice behind Auto-Split, an
edge-cloud collaborative prototype of Huawei Cloud. This patented technology is
already validated on selected applications, is on its way for broader
systematic edge-cloud application integration, and is being made available for
public use as an automated pipeline service for end-to-end cloud-edge
collaborative intelligence deployment. To the best of our knowledge, there is
no existing industry product that provides the capability of Deep Neural
Network (DNN) splitting.
