  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.
