The distributed embedded systems paradigm is a promising platform for high-performance embedded applications. We present a distributed algorithm and system based on cost-effective devices. The proof of concept shows how a parallelized approach leveraging a distributed embedded platform can address the computational of the Machine Learning K-Nearest Neighbors (K-NN) algorithm with large and heterogeneous datasets.

Scalable K-Nearest Neighbors Implementation using Distributed Embedded Systems / DE SIO, Corrado; Avignone, Andrea; Sterpone, Luca; Chiusano, Silvia. - (2024), pp. 314-315. (Intervento presentato al convegno CF' 24: 21st ACM International Conference on Computing Frontiers tenutosi a Ischia (ITA) nel May 7-9, 2024) [10.1145/3649153.3652994].

Scalable K-Nearest Neighbors Implementation using Distributed Embedded Systems

Corrado De Sio;Andrea Avignone;Luca Sterpone;Silvia Chiusano
2024

Abstract

The distributed embedded systems paradigm is a promising platform for high-performance embedded applications. We present a distributed algorithm and system based on cost-effective devices. The proof of concept shows how a parallelized approach leveraging a distributed embedded platform can address the computational of the Machine Learning K-Nearest Neighbors (K-NN) algorithm with large and heterogeneous datasets.
2024
979-8-4007-0597-7
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2988853