Adaptive Reconvergence-driven AIG Rewriting via Strategy Learning
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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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 7 of its 28 references that are in the map. First posted on arXiv 22 Dec 2023.
Builds on
7 of 28 references.
References · 7
- 2019DRiLLS: Deep Reinforcement Learning for Logic Synthesiscited by 47
- 2021BOiLS: Bayesian Optimisation for Logic Synthesiscited by 18
- 2018The EPFL Logic Synthesis Librariescited by 6
- 2019Approximate Logic Synthesis: A Reinforcement Learning-Based Technology Mapping Approachcited by 6
- 2023AISYN: AI-driven Reinforcement Learning-Based Logic Synthesis Frameworkcited by 4
- 2021Enhanced Fast Boolean Matching based on Sensitivity Signatures Pruningcited by 2
- 2022Rethinking Reinforcement Learning based Logic Synthesiscited by 2
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Citing papers per year: 2024: 2.
2 papers.