about
Deep Learning in the Era of Edge Computing (arxiv.org)
1 point by sebg on Oct 22, 2020 | hide | past | pdf | discuss on HN

In plain words: Devices that gather sensor data need to run deep learning locally, but training it demands lots of data and heavy computing, memory, and battery. This chapter lays out eight challenges and opportunities to make that possible, drawing on computer systems, networking, and machine learning.

Abstract · Deep Learning in the Era of Edge Computing: Challenges and Opportunities

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.

Mi Zhang, Faen Zhang, Nicholas D. Lane, Yuanchao Shu, Xiao Zeng, Biyi Fang, Shen Yan, Hui Xu
arXiv:2010.08861 · cs.LG · submitted Oct 17, 2020
abstract · pdf · html

add comment on HN