Auto-Split: A General Framework of Collaborative Edge-Cloud AI
자동 분할:협동 변두리 클 라 우 드 AI 의 유 니 버 설 프레임 워 크
División automática: un marco común para la Ia colaborativa Edge Cloud
Autosplit: un cadre commun pour la collaboration Edge Cloud AI
автоматическое разделение: общие рамки кооперированных облаков AI
Amin Banitalebi-Dehkordi ¹, Naveen Vedula ¹, Jian Pei 裴健 ², Fei Xia ³, Lanjun Wang ¹, Yong Zhang ¹
¹ Huawei Technologies Canada Co. Ltd. Vancouver, Canada
² School of Computing Science, Simon Fraser University, Vancouver, Canada
³ Huawei Technologies, Shenzhen, China
中国 深圳 华为技术有限公司
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 14 August 2021

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.
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining_1
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining_2
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining_3
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