BoolGebra: Attributed Graph-learning for Boolean Algebraic Manipulation

TL;DR

BoolGebra introduces a graph neural network-based method for Boolean algebraic manipulation, significantly improving scalability and efficiency in logic synthesis by reducing search space and enabling end-to-end optimization.

Contribution

It presents a novel attributed graph-learning approach combining GNNs and neural networks for scalable Boolean algebraic manipulation in logic synthesis.

Findings

Demonstrates generalizability across different designs.

Achieves scalable optimization from small to large datasets.

Integrates with ABC for end-to-end logic minimization.

Abstract

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.