Principal component analysis (PCA) is a powerful data reduction method for Structural Health Monitoring. However, its computational cost and data memory footprint pose a significant challenge when PCA has to run on limited capability embedded platforms in low-cost IoT gateways. This paper presents a memory-efficient parallel implementation of the streaming History PCA algorithm. On our dataset, it achieves 10× compression factor and 59× memory reduction with less than 0.15 dB degradation in the reconstructed signal-to-noise ratio (RSNR) compared to standard PCA. Moreover, the algorithm benefits from parallelization on multiple cores, achieving a maximum speedup of 4.8× on Samsung ARTIK 710.

Embedding Principal Component Analysis for Data Reduction in Structural Health Monitoring on Low-Cost IoT Gateways

Burrello A.;Marchioni A.;Brunelli D.;Benini L.
2019

Abstract

Principal component analysis (PCA) is a powerful data reduction method for Structural Health Monitoring. However, its computational cost and data memory footprint pose a significant challenge when PCA has to run on limited capability embedded platforms in low-cost IoT gateways. This paper presents a memory-efficient parallel implementation of the streaming History PCA algorithm. On our dataset, it achieves 10× compression factor and 59× memory reduction with less than 0.15 dB degradation in the reconstructed signal-to-noise ratio (RSNR) compared to standard PCA. Moreover, the algorithm benefits from parallelization on multiple cores, achieving a maximum speedup of 4.8× on Samsung ARTIK 710.
2019
ACM International Conference on Computing Frontiers 2019, CF 2019 - Proceedings
235
239
Burrello A.; Marchioni A.; Brunelli D.; Benini L.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/730303
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