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对模型压缩技术进行比较分析,以实现碳效率高的AI
Eileen Paula1, Jayesh Soni2, Himanshu Upadhyay3
1Applied Research Center, Florida International University, Miami, 33174, USA.
Scientific reports
|July 3, 2025
概括
模型压缩技术,如修剪和蒸,显著降低了变压器模型的能源消耗和碳排放. 这项研究表明,在不牺牲性能的情况下,实现可持续人工智能的可行途径.
科学领域:
- 人工智能的人工智能
- 计算机科学 计算机科学
- 环境科学 环境科学
背景情况:
- 像BERT这样的大型语言模型 (LLM) 的日益增长的计算需求引发了环境问题.
- 需要可持续的AI实践来减轻AI的碳足迹.
- 基于变压器的模型越来越普遍,但能源密集型.
研究的目的:
- 研究模型压缩技术 (修剪,知识蒸,量子化) 的效率,以减少能源消耗和碳排放.
- 评估这些技术对变压器模型的性能影响.
- 将压缩模型与固有的碳效率架构进行比较.
主要方法:
- 在BERT,DistilBERT,ALBERT和ELECTRA中应用了修剪,知识蒸和量子化.
- 使用亚马逊极性数据集进行情绪分析.
- 使用CodeCarbon工具测量了能源消耗和碳排放.
- 对压缩模型的性能指标进行比较 (准确性,精度,回忆,F1,ROC AUC).
主要成果:
- 实现了显著的能源减少:BERT (32.1%的修剪/蒸),DistilBERT (缺少特定的%,但减少了),ALBERT (7.12%的量化),ELECTRA (23.9%的修剪/蒸).
- 在大多数模型中,性能指标保持在95.87-99.06%的准确度,精度,回忆,F1和ROC AUC范围内.
- 阿尔伯特与量子化显示显著的性能退化,突出敏感性.
结论:
- 模型压缩技术有效地减少了基于变压器的AI模型对环境的影响.
- 可持续的AI实践可以通过高效的模型设计和优化来实现.
- 需要进一步的研究来解决某些架构中的量子化灵敏性.
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