A hybrid clustering algorithm for high-performance edge computing devices [Short]

Abstract

Clustering algorithms are efficient tools for discovering correlations or affinities within large datasets and are the basis of several Artificial Intelligence processes based on data generated by sensor networks. Recently, such algorithms have found an active application area closely correlated to the Edge Computing paradigm. The final aim is to transfer intelligence and decision-making ability near the edge of the sensors networks, thus avoiding the stringent requests for low-latency and large-bandwidth networks typical of the Cloud Computing model. In such a context, the present work describes a new hybrid version of a clustering algorithm for the NVIDIA Jetson Nano board by integrating two different parallel strategies. The algorithm is later evaluated from the points of view of the performance and energy consumption, comparing it with two high-end GPU-based computing systems. The results confirm the possibility of creating intelligent sensor networks where decisions are taken at the data collection points.