Estimation of energy consumption in machine learning - García-Martín, Eva; Rodrigues, Crefeda Faviola; Riley, Graham; Grahn, Håkan (Journal of parallel and distributed computing, 2019)
Energy consumption has been widely studied in the computer architecture field for decades. While the adoption of energy as a metric in machine learning is emerging, the majority of research is still primarily focused on obtaining high levels of accuracy without any computational constraint. We believe that one of the reasons for this lack of interest is due to their lack of familiarity with approaches to evaluate energy consumption. To address this challenge, we present a review of the different approaches to estimate energy consumption in general and machine learning applications in particular. Our goal is to provide useful guidelines to the machine learning community giving them the fundamental knowledge to use and build specific energy estimation methods for machine learning algorithms. We also present the latest software tools that give energy estimation values, together with two use cases that enhance the study of energy consumption in machine learning. •Literature review of energy estimation methods from computer architecture for machine learning applications.•State-of-the-art approaches to estimate energy consumption in machine learning.•Software tools from the power and performance monitoring field and their applicability to machine learning.•Two use cases to estimate energy consumption for deep learning and data mining.