论文标题
在边缘计算时代的深度学习：挑战和机遇
Deep Learning in the Era of Edge Computing: Challenges and Opportunities
论文作者
论文摘要
边缘计算的时代已经到来。尽管互联网是边缘计算的骨干，但其真实价值在于从传感器收集数据并从传感器数据中提取有意义的信息的相交。我们设想，在不久的将来，大多数边缘设备都将配备由深度学习提供支持的机器智能。但是，基于深度学习的方法需要大量的高质量数据才能训练，并且在计算，记忆和功耗方面非常昂贵。在本章中，我们描述了计算机系统，网络和机器学习的交集的八项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。我们希望本章能够激发新的研究，最终将导致实现智能优势的愿景。
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.
