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The mechanical efficiency of a machine is a fundamental concept that describes how effectively a machine can convert input work into output work. According to this concept, the efficiency of a machine is equal to the ratio of the output work to the input work. An ideal machine, meaning a machine that has no energy losses, has an efficiency of one. This implies that the input work and the output work are equal.
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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

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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.

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节能的人工智能 节能的人工智能模型的压缩压缩.在NLP模型中的可持续性.

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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.