Boolean decomposition for AIG optimization
Tipus de documentText en actes de congrés
EditorAssociation for Computing Machinery (ACM)
Condicions d'accésAccés obert
Restructuring techniques for And-Inverter Graphs (AIG), such as rewriting and refactoring, are powerful, scalable and fast, achieving highly optimized AIGs after few iterations. However, these techniques are biased by the original AIG structure and limited by single output optimizations. This paper investigates AIG optimization for area, exploring how far Boolean methods can reduce AIG nodes through local optimization.Boolean division is applied for multi-output functions using two-literal divisors and Boolean decomposition is introduced as a method for AIG optimization. Multi-output blocks are extracted from the AIG and optimized, achieving a further AIG node reduction of 7.76% on average for ITC99 and MCNC benchmarks.
CitacióMachado, L., Cortadella, J. Boolean decomposition for AIG optimization. A: ACM Great Lakes Symposium on VLSI. "Proceedings of the on Great Lakes Symposium on VLSI 2017". New York: Association for Computing Machinery (ACM), 10/05/2017, p. 143-148.
Versió de l'editorhttps://dl.acm.org/citation.cfm?id=3060420