A Novel Structural Choices Generation Method for Logic Restructuring
Zhang Hu, Chengyu Ma, Zhufei Chu · 2024
Logic restructuring is an efficient method for achieving conversions between different logic representations and opti-mizing logic network. However, as a mapping-based method, it often comes with structural bias issues. This will result in poor quality of logic restructuring. Therefore, we propose a novel structural choices generation method to address the issue of structural bias in logic restructuring. It matches the substructures of the network with the database of pre-computed optimum structures to obtain equivalent nodes. It can efficiently obtain many high-quality candidate structures and generate choice networks for different logic representations. The experimental results demonstrate that our method effectively improves the quality of results during logic transformation. In logic optimization, our approach achieves a 67.3 % reduction in logic depth for test cases, with a 1.95 % decrease in network size.