Adaptive Reconvergence-driven AIG Rewriting via Strategy Learning
This work addresses performance, power, and area optimization in circuit design, representing an incremental improvement over traditional methods.
The paper tackled the problem of optimizing logic synthesis for circuits by proposing an adaptive reconvergence-driven AIG rewriting algorithm that combines multi-strategy-based rewriting and strategy learning-based selection, resulting in an average improvement of 5.567% in size and 5.327% in depth.
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.