  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.
