Improving Performance and Energy Efficiency of the Classification of Data Streams on Edge Computing - Luna, Reginaldo; Cassales, Guilherme; Senger, Hermes (Anais da Escola Regional de Alto Desempenho de São Paulo (ERAD-SP); 2023: Anais da XIV Escola Regional de Alto Desempenho de São Paulo, 2023)
In this work, we propose a loop transformation to improve performance and energy efficiency of ensembles. We compare the performance of our technique with three other strategies for improving energy efficiency and throughput in data stream classification using six state-of-the-art ensemble algorithms and four benchmark datasets. Our results show that software strategies can significantly reduce energy consumption. Mini-batching improved energy efficiency by 96% on average and 169% in the best case. Likewise, mini-batching with loop fusion improved energy efficiency by 136% on average and 456% in the best case.