Towards a sustainable artificial intelligence: A case study of energy efficiency in decision tree algorithms

Abstract

Artificial intelligence has been showing accelerated growth due to its use in solving problems in several application domains. This success results from the convergence of large amounts of data, high‐performance computing, and precision of machine learning (ML) algorithms. Even with the relevance of ML algorithms, little is known about their computational requirements and power consumption, which has become an important task to achieve greener computing. This work aims to evaluate the energy efficiency of the ML algorithms to identify their energy hotspots. Also, to investigate which influences the energy consumption (EC) of these algorithms and how the parameters design could affect it. We conducted a series of experiments using 27 different datasets, 2 decision trees algorithms, and 2 ensembles for classification and regression tasks to answer these questions. Our results show interesting findings, such as, like some simple parameters choice can have a high impact on EC, as a consequence, finding greener strategies for AI.

Investigating hardware and software aspects in the energy consumption of machine learning: A green AI‐centric analysis

“…Software approach: Some software approaches for energy savings are the investigation of computational requirements and their influence on energy consumption with code optimizations, the influence of the number of instances and features in the overall energy consumption, 26 identifying which part of the algorithm is consuming most of the energy (hotspots), 27 hyperparameters choice and its impact on energy. 28 Regarding the library, Holt & Sievert 29 Another aspect related to the algorithms that have emerged as a promising design alternative to better performance and energy efficiency in ML is Approximate Computing (AC). Different techniques and designs have been proposed at both hardware and software levels.…”

“…Software approach: Some software approaches for energy savings are the investigation of computational requirements and their influence on energy consumption with code optimizations, the influence of the number of instances and features in the overall energy consumption, 26 identifying which part of the algorithm is consuming most of the energy (hotspots), 27 hyperparameters choice and its impact on energy 28 …”

Sustainable Artificial Intelligence Systems: An Energy Efficiency Approach

“…The parameter values selected may influence not only the quality of results but also power consumption. Similarly, in [45], the authors proposed a methodology to estimate the energy consumption of Decision Tree-based ML algorithms and to identify the most energyconsuming parts and parameters. They monitored the energy consumption for each function or piece of code using RAPL and showed that they could reduce the energy consumption without reducing accuracy.…”

Decision-Tree Based Methodology Aid in Assessing the Sustainable Development of a Manufacturing Company

“…Besides, the usage of a decision tree provides an opportunity to save time [11]. Literature analysis shows applications decision-tree (DT) based, decision support in the SD area [12]. The work aims to evaluate the energy efficiency of the Machine Learning algorithms to identify their energy hotspots.…”