Enabling Deep Learning on IoT Edge: Approaches and Evaluation

摘要

As we enter the Internet of Things (IoT) era, the size of mobile computing devices is largely reduced while their computing capability is dramatically improved. Meanwhile, machine learning technologies have been well developed and shown cutting edge performance in various tasks, leading to their wide adoption. As a result, moving machine learning, especially deep learning capability to the edge of the IoT is a trend happening today. But directly moving machine learning algorithms which originally run on PC platform is not feasible for IoT devices due to their relatively limited computing power. In this paper, we first reviewed several representative approaches for enabling deep learning on mobile/IoT devices. Then we evaluated the performance and impact of these methods on IoT platform equipped with integrated GPU and ARM processor. Our results show that we can enable the deep learning capability on the edge of the IoT if we apply these approaches in an efficient manner.