Conference Proceeding

Exploring the Accuracy - Energy Trade-off in Machine Learning



Brownlee A, Adair J, Haraldsson S & Jabbo J (2021) Exploring the Accuracy - Energy Trade-off in Machine Learning. In: 2021 IEEE/ACM International Workshop on Genetic Improvement (GI). Genetic Improvement Workshop at 43rd International Conference on Software Engineering, Madrid, Spain, 30.05.2021-30.05.2021. Piscataway, NJ: IEEE.

Machine learning accounts for considerable global electricity demand and resulting environmental impact, as training a large deep-learning model produces 284 000kgs of the greenhouse gas carbon dioxide. In recent years, search-based approaches have begun to explore improving software to consume less energy. Machine learning is a particularly strong candidate for this because it is possible to trade off functionality (accu-racy) against energy consumption, whereas with many programs functionality is simply a pass-or-fail constraint. We use a grid search to explore hyperparameter configurations for a multilayer perceptron on five classification data sets, considering trade-offs of classification accuracy against training or inference energy. On one data set, we show that 77% of energy consumption for inference can saved by reducing accuracy from 94.3% to 93.2%. Energy for training can also be reduced by 30-50% with minimal loss of accuracy. We also find that structural parameters like hidden layer size is a major driver of the energy-accuracy trade-off, but there is some evidence that non-structural hyperparameters influence the trade-off too. We also show that a search-based approach has the potential to identify these trade-offs more efficiently than the grid search.

Publication date31/12/2021
Publication date online07/07/2021
Related URLs
Place of publicationPiscataway, NJ
ConferenceGenetic Improvement Workshop at 43rd International Conference on Software Engineering
Conference locationMadrid, Spain

People (3)


Dr Jason Adair
Dr Jason Adair

Lecturer in Data Science, Computing Science

Dr Sandy Brownlee
Dr Sandy Brownlee

Senior Lecturer in Computing Science, Computing Science and Mathematics - Division

Dr Saemundur Haraldsson
Dr Saemundur Haraldsson

Lecturer, Computing Science