# BoolGebra: Attributed Graph-learning for Boolean Algebraic Manipulation

arXiv: https://arxiv.org/abs/2401.10753 · HTML: https://mlsanity.com/paper/2401.10753/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 41 references that are in the map.
- First posted on arXiv 19 Jan 2024.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [A Survey of Machine Learning for Computer Architecture and Systems](https://mlsanity.com/paper/2102.07952/) (2021)
- [DRiLLS: Deep Reinforcement Learning for Logic Synthesis](https://mlsanity.com/paper/1911.04021/) (2019)
- [Developing Synthesis Flows Without Human Knowledge](https://mlsanity.com/paper/1804.05714/) (2018)
- [Gamora: Graph Learning based Symbolic Reasoning for Large-Scale Boolean Networks](https://mlsanity.com/paper/2303.08256/) (2023)
- [IronMan: GNN-assisted Design Space Exploration in High-Level Synthesis via Reinforcement Learning](https://mlsanity.com/paper/2102.08138/) (2021)
- [HW2VEC: A Graph Learning Tool for Automating Hardware Security](https://mlsanity.com/paper/2107.12328/) (2021)
- [FlowTune: End-to-end Automatic Logic Optimization Exploration via Domain-specific Multi-armed Bandit](https://mlsanity.com/paper/2202.07721/) (2022)
- [RESPECT: Reinforcement Learning based Edge Scheduling on Pipelined Coral Edge TPUs](https://mlsanity.com/paper/2304.04716/) (2023)
- [DAG-aware Synthesis Orchestration](https://mlsanity.com/paper/2310.07846/) (2023)
- [Verilog-to-PyG -- A Framework for Graph Learning and Augmentation on RTL Designs](https://mlsanity.com/paper/2311.05722/) (2023)
- [Graph Neural Networks: A Powerful and Versatile Tool for Advancing Design, Reliability, and Security of ICs](https://mlsanity.com/paper/2211.16495/) (2022)

## Built on by (most cited first, 2 of 2)

- [MapTune: Advancing ASIC Technology Mapping via Reinforcement Learning Guided Library Tuning](https://mlsanity.com/paper/2407.18110/) (2024)
- [BoolSkeleton: Boolean Network Skeletonization via Homogeneous Pattern Reduction](https://mlsanity.com/paper/2511.02196/) (2025)

## Abstract (arXiv)

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.