深层卷积神经网络的结构化修剪:一项调查
IEEE transactions on pattern analysis and machine intelligence
|November 28, 2023
概括
深层卷积神经网络 (CNN) 的结构化修剪提供了高效的模型压缩. 这份调查详细介绍了最先进的结构化修剪技术,解决了实际加速的挑战和未来研究方向.
科学领域:
- 人工智能的人工智能
- 计算机科学 计算机科学
- 机器学习 机器学习
背景情况:
- 深度卷积神经网络 (CNN) 通过复杂的架构实现高性能,导致大量的计算成本.
- 神经网络修剪对于减少存储和计算需求至关重要.
- 结构化修剪提供了对硬件友好的加速,与非结构化重量修剪不同.
研究的目的:
- 为了调查深度CNN的结构化修剪的最新进展.
- 为了比较最先进的结构化修剪技术.
- 确定结构性修剪的挑战和未来的研究机会.
主要方法:
- 基于过器排名,规范化,动态执行和神经架构搜索的结构化修剪技术的比较.
- 讨论彩票假设和修剪应用.
- 简要介绍用于对比的非结构化修剪.
主要成果:
- 一个全面的概述当前的结构化修剪方法.
- 不同结构性修剪方法的比较分析.
- 确定该领域的关键挑战和创新解决方案.
结论:
- 结构化的修剪对于高效和实用的深度CNN加速至关重要.
- 该领域为进一步的研究和开发提供了许多机会.
- 进一步探索的资源包括精选的纸质列表和交互式比较网站.
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