Tactic: Choose an Energy Efficient Algorithm

Tactic sort: Awesome Tactic

Type: Architectural Tactic

Tags: algorithm-design machine-learning measured

Title

Choose an Energy Efficient Algorithm

Description

Different machine learning algorithms have different levels of energy consumption and computational power. For example, the K-nearest neighbor (KNN) algorithm has much lower energy consumption than the ensemble method Random Forest (RF) (Verdecchia et al., 2022). High energy consumption does not necessarily mean that those algorithms perform better or achieve higher accuracy levels than low-energy algorithms.

Participant

Data Scientist

Related software artifact

Algorithm

Context

Machine Learning

Software feature

Inference

Tactic intent

Improve energy efficiency by choosing an energy-efficient algorithm that can achieve wanted model outcomes

Target quality attribute

Energy Efficiency

Other related quality attributes

Accuracy

Measured impact

Choosing suitable, energy efficient algorithms that achieve wanted outcomes can reduce the energy consumption of ML models (Kaack et al., 2022)

Source

R. Verdecchia, L. Cruz, J. Sallou, M. Lin, J. Wickenden, and E. Hotellier, Data-Centric Green AI An Exploratory Empirical Study, in 2022 International Conference on ICT for Sustainability (ICT4S), Plovdiv, Bulgaria: IEEE, Jun. 2022, pp. 35–45. [DOI](https://doi.org/10.1109/ICT4S55073.2022.00015); Lynn H Kaack, Priya L Donti, Emma Strubell, George Kamiya, Felix Creutzig, and David Rolnick. 2022. Aligning Artificial Intelligence with Climate Change Mitigation. Nature Climate Change 12, 6 (2022), 518–527 [DOI](https://doi.org/10.1038/s41558-022-01377-7)