Boolean algebraic manipulation is at the core of logic synthesis in Electronic Design Automation (EDA) design flow. Existing methods struggle to fully exploit optimization opportunities, and often suffer from an explosive search space and limited scalability efficiency. This work presents BoolGebra, a novel attributed graph-learning approach for Boolean algebraic manipulation that aims to improve fundamental logic synthesis. BoolGebra incorporates Graph Neural Networks (GNNs) and takes initial feature embeddings from both structural and functional information as inputs. A fully connected neural network is employed as the predictor for direct optimization result predictions, significantly reducing the search space and efficiently locating the optimization space. The experiments involve training the BoolGebra model w.r.t design-specific and cross-design inferences using the trained model, where BoolGebra demonstrates generalizability for cross-design inference and its potential to scale from small, simple training datasets to large, complex inference datasets. Finally, BoolGebra is integrated with existing synthesis tool ABC to perform end-to-end logic minimization evaluation w.r.t SOTA baselines.

翻译：布尔代数是电子设计自动化（EDA）设计流程中逻辑综合的核心。现有方法难以充分挖掘优化机会，且通常面临搜索空间爆炸和可扩展性效率受限的问题。本文提出BoolGebra，一种新颖的面向布尔代数操作的属性图学习方法，旨在改进基础逻辑综合。BoolGebra融合图神经网络（GNN），将来自结构与功能信息的初始特征嵌入作为输入，并采用全连接神经网络作为预测器直接预测优化结果，从而显著缩减搜索空间并高效定位优化空间。实验针对设计特定推理和跨设计推理两类场景训练BoolGebra模型，结果表明该模型在跨设计推理中具备泛化能力，且具有从小规模简单训练数据集扩展至大规模复杂推理数据集的潜力。最后，将BoolGebra与现有综合工具ABC集成，基于当前最优基线进行端到端逻辑最小化评估。