Abstract-The number of connected IoT devices is expected to reach over 20 billion by 2020. These range from basic sensor nodes that log and report the data for cloud processing, to the ones on the edge, that are capable of processing and analyzing the incoming information and taking an action accordingly. Machine learning, and in particular deep learning, is the defacto processing paradigm for intelligently processing these immense volumes of data. However, the resource inhibited environment of edge devices, owing to their limited energy budget, and low compute capabilities, render them a challenging platform for deployment of desired data analytics, particularly in realtime applications. In this paper therefore, we argue that for a wide range of emerging applications edge intelligence is a necessary evolutionary need, and thus we provide a summary of the challenges and opportunities that arise from this need. We showcase through a case study regarding computer vision for commercial drones, how these opportunities can be taken advantage, and how some of the challenges can be potentially addressed.
In REF the authors use the concept of near sensors for machine-learning applications, arguing that a wide range of emerging edge intelligence applications is a necessary evolution towards replacing cloud computing applications.
Edge Intelligence: Challenges and Opportunities of Near-Sensor Machine Learning Applications
2018 IEEE 29th International Conference on Application-specific Systems, Architectures and Processors (ASAP)
2018 IEEE 29th International Conference on Application-specific Systems, Architectures and Processors (ASAP)
Computer Science