Investigating hardware and software aspects in the energy consumption of machine learning: A green AI-centric analysis
Much has been discussed about artificial intelligence's negative environmental impacts due to its power-hungry Machine Learning algorithms and CO2\ CO_2 \ emissions linked to this. This work discusses three direct impacts of AI on energy consumption associated with computation: the software, the hardware, and the energy source's carbon intensity. We present an up-to-date revision of the literature and assess it through experiments. For hardware, we evaluate the use of ARM-based single-board computers for training Machine Learning algorithms. An experimental setup was developed training the algorithm XGBoost and its cost-effectiveness (energy consumption, acquisition cost, and execution time) compared with the X86-64 and GPU architectures and other algorithms. In addition, the CO2\ CO_2 \ is estimated for these experiments and compared for three energy sources. The results show that this type of architecture can become a viable and greener alternative, not only for inference but also for training these algorithms. Finally, we evaluated low precision for training Random Forest algorithms with different datasets for the software aspect. Results show that is possible energy reduction with no decrease in accuracy.
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