计算机器学习压缩算法的碳排放盈亏点，评估其环保效益。
Green BOA: Determining the environmental break-even point for ML-based data compression
- 以机器学习无损压缩为例，对比训练推理碳排与存储节省碳排。
- 首次量化了模型训练碳排与存储节约之间的环保盈亏平衡点。
- 适合关注绿色AI、可持续计算的研究者和工程师参考。
本文总结了曼彻斯特大学暑期实习项目的成果，聚焦于基于机器学习的数据压缩算法在环境可持续性方面的盈亏平衡点。以一个基于机器学习的无损压缩算法为例，我们比较了模型训练与推理所需的基础设施碳排放量，与因减少磁盘存储需求所带来的碳减排量，并讨论了两者的平衡点。研究结果为评估机器学习压缩技术的环境影响提供了量化基准。
原文摘要 · Abstract (English)
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.
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