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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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经过结构化的稀疏性修剪的中间粒度的核元素.

Peng Zhang1, Liang Zhao1, Cong Tian1

  • 1School of Computer Science and Technology, Xidian University, No. 2 South Taibai Road, Xi'an, 710071, PR China.

Neural networks : the official journal of the International Neural Network Society
|September 14, 2024
PubMed
概括
此摘要是机器生成的。

核心元素修剪 (KEP) 为深层卷积神经网络提供了一种新的结构化修剪方法. 这种技术可以实现高压缩率,精度损失最小,非常适合资源有限的设备.

关键词:
深度神经网络是一种深度神经网络.模型修剪 模型修剪规范化 规范化 规范化稀疏的加速器加速器.

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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:

  • 深层卷积神经网络 (CNN) 需要大量的计算资源,这阻碍了在嵌入式或移动设备上部署.
  • 当前的结构化修剪方法往往会导致在高修剪速度下不可接受的精度下降.
  • 需要结构化的修剪技术,以平衡高压缩比,对分类准确性的影响最小.

研究的目的:

  • 引入内核元素修剪 (KEP),这是一个新的结构化修剪方法,用于CNNs.
  • 开发一种技术,以最小的精度下降实现高修剪率.
  • 为了确保结构化修剪在不同硬件架构中的普遍适用性.

主要方法:

  • KEP 探索每个内核层中的单个元素的重要性,以识别和删除不重要的权重.
  • 使用可控制的规范化处罚,使用先前知识面具来实现模型紧性.
  • 引入了一个稀疏的卷积操作,与传统的滑动窗口不同,以消除前向推理期间的冗余计算,优化FPGA部署.

主要成果:

  • 在CIFAR-10和ImageNet数据集上,KEP已经证明了它的有效性.
  • 与最先进的结构化方法相比,该方法显著减少了参数和浮点运算 (FLOP).
  • 即使在较少的非零重量下,KEP也保持了强的性能,这表明有效的压缩.

结论:

  • KEP是一种有效的结构化修剪技术,用于压缩深层卷积神经网络.
  • 该方法实现了优越的参数和FLOP的减少,同时保持了分类准确性.
  • 凯普的稀疏卷积操作为部署在像FPGA这样的硬件加速器上提供了优势.