# Adaptive Reconvergence-driven AIG Rewriting via Strategy Learning

arXiv: https://arxiv.org/abs/2312.14536 · HTML: https://mlsanity.com/paper/2312.14536/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 28 references that are in the map.
- First posted on arXiv 22 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [DRiLLS: Deep Reinforcement Learning for Logic Synthesis](https://mlsanity.com/paper/1911.04021/) (2019)
- [BOiLS: Bayesian Optimisation for Logic Synthesis](https://mlsanity.com/paper/2111.06178/) (2021)
- [The EPFL Logic Synthesis Libraries](https://mlsanity.com/paper/1805.05121/) (2018)
- [Approximate Logic Synthesis: A Reinforcement Learning-Based Technology Mapping Approach](https://mlsanity.com/paper/1902.00478/) (2019)
- [AISYN: AI-driven Reinforcement Learning-Based Logic Synthesis Framework](https://mlsanity.com/paper/2302.06415/) (2023)
- [Enhanced Fast Boolean Matching based on Sensitivity Signatures Pruning](https://mlsanity.com/paper/2111.06213/) (2021)
- [Rethinking Reinforcement Learning based Logic Synthesis](https://mlsanity.com/paper/2205.07614/) (2022)

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

- [OpenLS-DGF: An Adaptive Open-Source Dataset Generation Framework for Machine Learning Tasks in Logic Synthesis](https://mlsanity.com/paper/2411.09422/) (2024)
- [Boolean-aware Boolean Circuit Classification: A Comprehensive Study on Graph Neural Network](https://mlsanity.com/paper/2411.10481/) (2024)

## Abstract (arXiv)

Rewriting is a common procedure in logic synthesis aimed at improving the performance, power, and area (PPA) of circuits. The traditional reconvergence-driven And-Inverter Graph (AIG) rewriting method focuses solely on optimizing the reconvergence cone through Boolean algebra minimization. However, there exist opportunities to incorporate other node-rewriting algorithms that are better suited for specific cones. In this paper, we propose an adaptive reconvergence-driven AIG rewriting algorithm that combines two key techniques: multi-strategy-based AIG rewriting and strategy learning-based algorithm selection. The multi-strategy-based rewriting method expands upon the traditional approach by incorporating support for multi-node-rewriting algorithms, thus expanding the optimization space. Additionally, the strategy learning-based algorithm selection method determines the most suitable node-rewriting algorithm for a given cone. Experimental results demonstrate that our proposed method yields a significant average improvement of 5.567% in size and 5.327% in depth.