Problem
Research questions and friction points this paper is trying to address.
ML-based data compression
environmental sustainability
break-even point
carbon-equivalent
Green BOA: Determining the environmental break-even point for ML-based data compression
🤖 AI Summary
研究通过比较机器学习数据压缩算法的训练和推理基础设施碳排放与减少磁盘存储需求所节省的碳排放，确定了环境平衡点。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
ML-based data compression
environmental sustainability
break-even point
carbon-equivalent
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