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arXiv · 2312.14536

Adaptive Reconvergence-driven AIG Rewriting via Strategy Learning

Liwei Ni·Zonglin Yang·Jiaxi Zhang·Junfeng Liu·Huawei Li·Biwei Xie·Xinquan Li
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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.

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Liwei Ni, Zonglin Yang, Jiaxi Zhang, Junfeng Liu, Huawei Li, Biwei Xie, Xinquan Li. 2023-12-22. Adaptive Reconvergence-driven AIG Rewriting via Strategy Learning. https://arxiv.org/abs/2312.14536

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