Adaptive Reconvergence-driven AIG Rewriting via Strategy Learning
Abstract
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.
Cite
@article{arxiv.2312.14536,
title = {Adaptive Reconvergence-driven AIG Rewriting via Strategy Learning},
author = {Liwei Ni and Zonglin Yang and Jiaxi Zhang and Junfeng Liu and Huawei Li and Biwei Xie and Xinquan Li},
journal= {arXiv preprint arXiv:2312.14536},
year = {2023}
}
Comments
The 41st IEEE International Conference on Computer Design