Deep Learning in the Era of Edge Computing: Challenges and Opportunities
This work addresses the problem of enabling deep learning on resource-constrained edge devices for applications in IoT and smart systems, but it is incremental as it reviews existing challenges without presenting new solutions.
The paper identifies challenges in applying deep learning to edge computing, such as high resource demands, and outlines eight research opportunities to enable resource-limited edge devices to leverage deep learning capabilities.
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.