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对模型压缩技术的调查:过去,现在和未来
Defu Liu1, Yixiao Zhu1, Zhe Liu1
1Intelligent Game and Decision Lab (IGDL), Beijing, China.
Frontiers in robotics and AI
|April 4, 2025
概括
大型模型需要大量资源,使得模型压缩对于高效部署至关重要. 本综述探讨了量子化和修剪等技术,以克服这些挑战.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机科学 计算机科学
背景情况:
- 一般用途的大型模型表现出卓越的性能,推动了特定领域的模型开发.
- 大型模型在训练和推理过程中需要大量的计算资源和内存.
- 大型模型的实际部署面临着重大的硬件和资源挑战.
研究的目的:
- 提供对模型压缩技术的全面审查.
- 探索模型压缩的基本原则,最近的进展和创新策略.
- 提供对实际应用的见解和有效的模型部署的未来方向.
主要方法:
- 模型压缩演变的审查.
- 详细检查量子化,修剪,低级分解和知识蒸.
- 分析这些方法的最新进展和创新策略.
主要成果:
- 模型压缩对于克服大型模型的资源限制至关重要.
- 量子化和修剪等各种技术为高效部署提供了途径.
- 该审查综合了当前的知识,并确定了未来的研究方向.
结论:
- 模型压缩对于大型人工智能模型的实用和广泛部署至关重要.
- 对研究人员和从业人员来说,了解各种压缩策略是关键.
- 本综述为推进高效的人工智能模型开发和应用提供了基础资源.
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