关于深度神经网络修剪的调查:分类学,比较,分析和建议
IEEE transactions on pattern analysis and machine intelligence
|August 21, 2024
概括
本调查回顾了用于压缩大型模型的深度神经网络修剪技术. 它分类方法,比较方法,并确定未来的研究方向,以有效地部署人工智能.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 现代深度神经网络 (DNN),特别是大型语言模型 (LLM),需要大量的计算和存储资源.
- 像修剪这样的模型压缩技术对于在资源有限的环境中部署DNN和加速推断至关重要.
- 尽管最近发表了3000多篇修剪论文,但缺乏全面的,最新的审查.
研究的目的:
- 为深度神经网络修剪研究提供全面的审查.
- 建立一个分类系统,以了解修剪技术.
- 确定模型压缩领域的新兴趋势和未来研究方向.
主要方法:
- 将剪裁研究分为四个主要领域:加速度类型,剪裁时间,剪裁方法和与其他压缩技术的整合.
- 对八个关键修剪策略对进行比较分析 (例如,非结构化与结构化,一次性与代).
- 探索LLM,视觉变压器,扩散模型和多式模式中的修剪应用,以及后培训和监督修剪方法.
主要成果:
- 现有的深度神经网络修剪研究的结构化概述.
- 对不同修剪策略及其权衡的比较见解.
- 识别新兴主题及其具体挑战和机会.
结论:
- 该调查通过突出共同点和差异,为开发新的修剪方法提供了基础.
- 为选择合适的修剪技术提供了建议.
- 概述了神经网络修剪的有希望的未来研究途径,包括对抗性强度和自然语言理解的应用.
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