Auto-Split: A General Framework of Collaborative Edge-Cloud AI

TL;DR

Auto-Split introduces a novel edge-cloud collaborative framework for deploying deep neural networks efficiently across resource-constrained edge devices and powerful cloud servers, maintaining high accuracy and low latency.

Contribution

It presents the first industry-ready, patented DNN splitting technology enabling automated, end-to-end edge-cloud collaborative AI deployment at scale.

Findings

Validated on multiple applications

Supports broad industry integration

Available as an automated deployment pipeline

Abstract

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.