Green BOA: Determining the environmental break-even point for ML-based data compression
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Computer Science > Machine Learning
Title: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.
| Comments: | 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 |
| Subjects: | Machine Learning (cs.LG); High Energy Physics - Experiment (hep-ex); Computational Physics (physics.comp-ph) |
| Report number: | LOCO2026/L05 |
| Cite as: | arXiv:2608.19994 [cs.LG] |
| (or arXiv:2608.19994v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.19994
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