边缘智能：边缘计算与人工智能融合的新范式

周知; 于帅; 陈旭

doi:10.11959/j.issn.2096-0271.2019013

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边缘智能：边缘计算与人工智能融合的新范式

专题：边缘计算 | 更新时间：2024-06-03

- 边缘智能：边缘计算与人工智能融合的新范式
- Edge intelligence:a new nexus of edge computing and artificial intelligence
- 大数据 2019年5卷第2期页码：2019013-1
- 作者机构：
- 作者简介：
  
  [ "周知（1993- ），男，中山大学数据科学与计算机学院特聘研究员，主要研究方向为云计算、边缘计算和分布式系统。" ]
  [ "于帅（1986- ），男，博士，中山大学数据科学与计算机学院在站博士后，主要研究方向为无线通信、移动计算、机器学习等。" ]
  [ "陈旭（1986- ），男，中山大学数据科学与计算机学院教授、博士生导师，主要研究方向为边缘计算、边缘智能、智能物联网等。" ]
- 基金信息：
  
  国家重点研发计划基金资助项目;National Key Research and Development Program of China(2017YFB1001703);国家自然科学基金资助项目;The National Natural Science Foundation of China(U1711265);国家自然科学基金资助项目;The National Natural Science Foundation of China(61802449);广东省引进创新创业团队基金资助项目;Program for Guangdong Introducing Innovative and Entrepreneurial Teams(2017ZT07X355);广东省自然科学基金资助项目;Guangdong Natural Science Funds(2018A030313032)
- DOI：10.11959/j.issn.2096-0271.2019013
  中图分类号： TP393
- 网络首发：2019-03，
  
  纸质出版：2019-03-15
- 稿件说明：
移动端阅览
周知, 于帅, 陈旭. 边缘智能：边缘计算与人工智能融合的新范式[J]. 大数据, 2019,5(2):2019013-1.

Zhi ZHOU, Shuai YU, Xu CHEN. Edge intelligence:a new nexus of edge computing and artificial intelligence[J]. Big Data Research, 2019, 5(2): 2019013-1.
周知, 于帅, 陈旭. 边缘智能：边缘计算与人工智能融合的新范式[J]. 大数据, 2019,5(2):2019013-1. DOI： 10.11959/j.issn.2096-0271.2019013.

Zhi ZHOU, Shuai YU, Xu CHEN. Edge intelligence:a new nexus of edge computing and artificial intelligence[J]. Big Data Research, 2019, 5(2): 2019013-1. DOI： 10.11959/j.issn.2096-0271.2019013.

摘要

边缘计算与人工智能这两种高速发展的新技术之间存在着彼此赋能的巨大潜力。通过3个研究案例，展示协同边缘计算和人工智能这两种技术如何促进各自的进一步发展。从边缘计算赋能人工智能的维度，针对深度学习模型在网络边缘侧的部署，提出了基于边端协同的深度学习按需加速框架，通过协同优化模型分割和模型精简策略，实现时延约束下的高精度模型推理。从人工智能赋能边缘计算的维度，针对边缘计算服务的放置问题，提出了基于在线学习的自适应边缘服务放置机制和基于因子图模型的预测性边缘服务迁移方法。

Abstract

Artificial intelligence (AI) and edge computing (EC) represent two of today’s most popular technologies.There is a great potential to coordinate these two emerging techniques to facilitate the further advent of both sides.Through three research cases

the profound benefits were demonstrated when AI and EC synergize.Specifically

from the perspective of EC for AI

to efficiently run deep learning at the network edge

a collaborative and on-demand deep neural network (DNN) co-inference framework with device-edge synergy was proposed.By applying DNN partitioning and right-sizing

it minimizes the inference latency under target accuracy.On the other hand

from the perspective of AI for EC

for the dynamical placement of edge computing services

two methods were proposed:an online-learning based adaptive service migration strategy and a factor graph model driven predictive service migration technique.

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references

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