用图神经网络研究布尔电路分类，发现功能等价性可提升识别精度
Boolean-aware Boolean Circuit Classification: A Comprehensive Study on Graph Neural Network
- 基于布尔感知特性定义可互换的电路等价类
- 实验证明图神经网络能有效捕捉电路功能与结构关联
- 为逻辑综合中的电路匹配提供新思路，适合芯片设计研究者
布尔电路是由动态有向图结构和静态功能构成的计算图。常用的逻辑优化与布尔匹配变换可在逻辑综合中改变电路的行为，同时影响其图结构与功能。基于图结构的布尔电路分类可归为图分类任务，但基于功能的分类仍是开放问题。本文首次基于“布尔感知”特性定义了匹配等价类，该类中的布尔电路可通过变换相互转化。我们提出一个基于图神经网络（GNN）的通用分析框架，用于研究影响布尔感知电路分类的关键因素。实验结果验证了分析的有效性，并指出了改进方向与潜在机会。代码与数据集将在论文被接受后公开。
原文摘要 · Abstract (English)
Boolean circuit is a computational graph that consists of the dynamic directed graph structure and static functionality. The commonly used logic optimization and Boolean matching-based transformation can change the behavior of the Boolean circuit for its graph structure and functionality in logic synthesis. The graph structure-based Boolean circuit classification can be grouped into the graph classification task, however, the functionality-based Boolean circuit classification remains an open problem for further research. In this paper, we first define the proposed matching-equivalent class based on its ``Boolean-aware'' property. The Boolean circuits in the proposed class can be transformed into each other. Then, we present a commonly study framework based on graph neural network~(GNN) to analyze the key factors that can affect the Boolean-aware Boolean circuit classification. The empirical experiment results verify the proposed analysis, and it also shows the direction and opportunity to improve the proposed problem. The code and dataset will be released after acceptance.
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