Deep Learning With Analytics on Edge

Kavita Srivastava (Institute of Information Technology and Management, GGSIP University, India)
Copyright: © 2021 |Pages: 133
EISBN13: 9781799885719|DOI: 10.4018/978-1-7998-4873-8.ch006
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The steep rise in autonomous systems and the internet of things in recent years has influenced the way in which computation has performed. With built-in AI (artificial intelligence) in IoT and cyber-physical systems, the need for high-performance computing has emerged. Cloud computing is no longer sufficient for the sensor-driven systems which continuously keep on collecting data from the environment. The sensor-based systems such as autonomous vehicles require analysis of data and predictions in real-time which is not possible only with the centralized cloud. This scenario has given rise to a new computing paradigm called edge computing. Edge computing requires the storage of data, analysis, and prediction performed on the network edge as opposed to a cloud server thereby enabling quick response and less storage overhead. The intelligence at the edge can be obtained through deep learning. This chapter contains information about various deep learning frameworks, hardware, and systems for edge computing and examples of deep neural network training using the Caffe 2 framework.
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