Abstract
The growing energy consumption caused by IT is forcing application developers to consider energy efficiency as one of the fundamental design parameters. This parameter acquires great relevance in HPC systems when running artificial neural networks and Machine Learning applications. Thus, this article shows an example of how to estimate and consider energy consumption in a real case of EEG classification. An efficient and distributed implementation of the KNN algorithm that uses mRMR as a feature selection technique to reduce the dimensionality of the dataset is proposed. The performance of three different workload distributions is analyzed to identify which one is more suitable according to the experimental conditions. The proposed approach outperforms the classification results obtained by previous works. It achieves an accuracy rate of 88.8% and a speedup of 74.53 when running on a multi-node heterogeneous cluster, consuming only 13.38% of the energy of the sequential version.
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Acknowledgements
Work funded by the Spanish Ministry of Science, Innovation, and Universities under grants PGC2018-098813-B-C31, PID2022-137461NB-C32 and ERDF funds. Also, the authors would like to thank Dr. Alberto Prieto, from the Department of Computer Engineering, Automation, and Robotics of the University of Granada, Spain, for his valuable collaboration in this work.
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Escobar, J.J. et al. (2023). Energy-Aware KNN for EEG Classification: A Case Study in Heterogeneous Platforms. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2023. Lecture Notes in Computer Science, vol 14134. Springer, Cham. https://doi.org/10.1007/978-3-031-43085-5_40
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