Automatic channel pruning by neural network based on improved poplar optimisation algorithm

Hu, Yuanxu

doi:10.1016/j.knosys.2025.113002

2025-02-01

PRE

DOI：10.1016/j.knosys.2025.113002

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卷积神经网络（CNNs）被广泛应用于计算机视觉和自然语言处理等多个领域。然而，参数数量和内存需求的增加给其在边缘设备上的部署带来了挑战。研究表明，CNNs存在冗余参数，基于重要性剪枝方法的准则繁琐，且现有的自动剪枝算法难以有效捕获全局特征。为解决此类方法的不足，本文提出了一种基于改进的杨树优化算法（ACPPO）的自动通道剪枝方法。本研究旨在利用聚类和优化算法搜索最优网络结构。首先，采用基于密度的层次聚类对每个卷积层的通道进行相似性分组，从而形成初步的压缩结构。随后，利用改进的杨树优化算法（POA）通过迭代优化搜索确定最优紧凑网络结构。在POA的无性繁殖过程中，使用局部最优值替代原始随机历史信息以引导个体搜索方向，从而增强种群多样性并提高搜索效率和全局搜索能力。本文在两个常用图像分类数据集CIFAR-10和ILSVRC2012上验证了ACPPO的有效性。在CIFAR-10上，ACPPO将GoogLeNet网络的参数量减少69.37%，FLOPs减少75.33%，相比基准模型准确率提升0.13%。

Keyword:

Convolutional neural networks

Automatic channel pruning

HDBSCAN clustering

Poplar optimisation algorithm

期刊

Knowledge-Based Systems

IF:

7.6

论文数:

1.2W

被引数:

4.5W

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