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.

本文介绍了 BoolGebra，一种新的属性图学习方法，用于布尔代数运算，旨在改进基本逻辑综合。BoolGebra 采用图神经网络和从结构和功能信息中提取的初始特征嵌入作为输入。使用全连接神经网络作为预测器进行直接优化结果预测，显著减小了搜索空间，并高效定位优化空间。实验中使用训练模型对特定设计和跨设计推理进行训练，BoolGebra 展示了跨设计推理的通用性和从小型简单训练数据集到大型复杂推理数据集的可扩展性。最后，BoolGebra 与现有综合工具 ABC 集成，实施端到端逻辑最小化评估，与 SOTA 基线相比。