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

• URL: http://arxiv.org/abs/2010.08861v1
• Date: Sat, 17 Oct 2020 20:50:42 GMT
• Title: Deep Learning in the Era of Edge Computing: Challenges and Opportunities
• Authors: Mi Zhang, Faen Zhang, Nicholas D. Lane, Yuanchao Shu, Xiao Zeng, Biyi Fang, Shen Yan, Hui Xu
• Abstract summary: We envision that in the near future, majority of edge devices will be equipped with machine intelligence powered by deep learning. 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.
• Score: 21.638476468152312
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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