Green BOA: Determining the environmental break-even point for ML-based data compression

Abstract:We summarise the outcome of two summer internship projects based at the University of Manchester, focused on the break-even point in terms of environmental sustainability for ML-based data compression algorithms. Using the example of a ML-based lossless compression algorithm, we compare estimates for the carbon-equivalent of the infrastructure needed for ML training and inference with the carbon-equivalent savings from reduced disk storage requirements, and discuss their break-even point.

3 pages, 1 figure. Accepted as a lightning-talk contribution at the 2nd International Workshop on Low Carbon Computing (LOCO 2026), Lancaster University, United Kingdom, 10-11 September 2026. Part of the LOCO 2026 proceedings, arXiv:LOCO2026/L05

Machine Learning (cs.LG); High Energy Physics - Experiment (hep-ex); Computational Physics (physics.comp-ph)

arXiv:2608.19994 [cs.LG]

(or arXiv:2608.19994v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.19994 arXiv-issued DOI via DataCite (pending registration)