Energy-aware KNN for EEG Classification: A Case Study in Heterogeneous Platforms Escobar Pérez, Juan José Rodríguez, Francisco Savran Kiziltepe, Rukiye Prieto Campos, Beatriz Kimovski, Dragi Ortiz, Andrés Damas Hermoso, Miguel Parallel and distributed programming Heterogeneous clusters Energy-aware computing EEG classification KNN mRMR 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. 2024-05-23T09:37:33Z 2024-05-23T09:37:33Z 2023-09-30 conference output 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 https://hdl.handle.net/10481/92001 10.1007/978-3-031-43085-5_40 eng http://creativecommons.org/licenses/by-nc-nd/3.0/ open access Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License