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在生物医学本体学对齐中搜索优化的量化.

Oussama Bouaggad1,2, Natalia Grabar1

  • 1CNRS, Univ. Lille, UMR 8163 - STL - Savoirs Textes Langage, Lille, France.

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概括
此摘要是机器生成的。

这项研究优化了大型人工智能模型,用于资源有限的环境,使用语义相似性和先进的定量化技术. 该研究实现了显著的加速和减少内存使用,同时保持生物医学任务的性能.

关键词:
这是一本UMLS Metathesaurus.模型优化 模型优化模型定量化定量化模型在本体学对齐对齐.语义上的相似性是语义上的相似性.变压器模型 变压器模型

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科学领域:

  • 人工智能的人工智能
  • 生物医学信息学 生物医学信息学
  • 计算科学 计算科学

背景情况:

  • 由于大小和计算需求,大型AI模型在资源有限的环境中存在部署挑战.
  • 有效的模型优化对于边缘设备至关重要,解决能源,内存和延迟问题.
  • 基于变压器的模型越来越复杂,需要先进的优化策略.

研究的目的:

  • 通过基于变压器的模型引入一种系统的方法来实现本体统一.
  • 优化AI模型,以便在边缘设备上有效部署.
  • 在减少资源消耗的生物医学任务中实现最先进的性能.

主要方法:

  • 员工监督的基于最先进的变压器模型进行本体学调整.
  • 在生物医学非专业人士的词汇和统一的医学语言系统 (UMLS) Metathesaurus之间利用了基于 cosine 的语义相似性.
  • 利用微软橄,ONNX运行时,英特尔神经压缩器和英特尔扩展PyTorch (IPEX) 来通过动态定量化进行模型优化.

主要成果:

  • 在DEFT 2020评估活动中的两个任务上取得了新的最先进的表现.
  • 实现了平均推论加快速度的20倍.
  • 减少了70%的内存使用量,同时保留了性能指标.

结论:

  • 提出的系统方法有效地优化了大型人工智能模型,用于资源有限的环境.
  • 在生物医学信息学中有效地部署人工智能模型是可行的,具有显著的性能增长.
  • 像动态量子化这样的高级优化技术是克服人工智能部署挑战的关键.