Deep Learning in the Era of Edge Computing: Challenges and Opportunities
Deep Learning in the Era of Edge Computing: Challenges and Opportunities
复制标题
边缘计算时代的深度学习：挑战与机遇
DOI：
10.1002/9781119551713.ch3
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Hui Xu
中科院分区：
文献类型：
--
作者：
Mi Zhang;Faen Zhang;N. Lane;Yuanchao Shu;Xiao Zeng;Biyi Fang;Shen Yan;Hui Xu
边缘计算时代已经到来。虽然互联网是边缘计算的支柱，但其真正的价值在于从传感器收集数据和从传感器数据中提取有意义的信息的交叉点。我们预计，在不久的将来，大多数边缘设备将配备由深度学习驱动的机器智能。然而，基于深度学习的方法需要大量高质量的数据来训练，并且在计算、内存和功耗方面非常昂贵。在本章中，我们描述了计算机系统、网络和机器学习交叉领域的八个研究挑战和有希望的机会。解决这些挑战将使资源有限的边缘设备能够利用深度学习的惊人能力。我们希望这一章能够激发新的研究，最终实现智能边缘的愿景。
The era of edge computing has arrived. Although the Internet is the backbone of edge computing, its true value lies at the intersection of gathering data from sensors and extracting meaningful information from the sensor data. We envision that in the near future, majority of edge devices will be equipped with machine intelligence powered by deep learning. However, deep learning-based approaches require a large volume of high-quality data to train and are very expensive in terms of computation, memory, and power consumption. In this chapter, we describe eight research challenges and promising opportunities at the intersection of computer systems, networking, and machine learning. Solving those challenges will enable resource-limited edge devices to leverage the amazing capability of deep learning. We hope this chapter could inspire new research that will eventually lead to the realization of the vision of intelligent edge.