MCE-HGCN:用于模拟IC匹配约束提取异质图形卷积网络
Yong Zhang1,2, Yong Yin1,2, Ning Xu1
1School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China.
Micromachines
|June 27, 2025
概括
我们开发了一个新的图形神经网络 (MCE-HGCN) 来有效地从模拟集成电路 (IC) 网列中提取匹配约束. 该方法通过准确识别关键匹配约束来改进模拟IC布局设计.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 匹配约束对于模拟集成电路 (IC) 布局性能至关重要.
- 当前从网列表中提取这些约束的方法可能是低效的.
- 精确的提取对于优化电路性能和缩短设计时间至关重要.
研究的目的:
- 提出一种新的方法,从模拟IC网清单中准确有效地提取匹配约束.
- 引入异质匹配约束提取图神经网络 (MCE-HGCN).
- 为了提高模拟IC布局设计的优化.
主要方法:
- 将网清单映射到异质属性多图中.
- 开发一个混合域的注意力机制,以利用图形拓和节点属性.
- 使用支向量机器 (SVM) 来匹配约束分类.
- 使用匹配过器去除干扰.
主要成果:
- 在MCE-HGCN模型中,即使在小型数据集中,也能显示出有效的融合.
- 在各种网列和电路类型的匹配预测中获得了0.917的F1平均得分.
- 通过包括注意力机制和匹配波器,显著提高了性能.
结论:
- MCE-HGCN准确有效地从各种模拟电路和过程中提取匹配约束.
- 该方法为模拟IC布局中的放置指导提供了宝贵的见解.
- MCE-HGCN提高了模拟IC布局设计的整体效率.
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