Boolean decomposition for AIG optimization

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Título:	Boolean decomposition for AIG optimization
Autor/a:	Machado, Lucas; Cortadella, Jordi
Otros autores:	Universitat Politècnica de Catalunya. Departament de Ciències de la Computació; Universitat Politècnica de Catalunya. ALBCOM - Algorismia, Bioinformàtica, Complexitat i Mètodes Formals
Abstract:	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.
Abstract:	Peer Reviewed
Materia(s):	-Àrees temàtiques de la UPC::Enginyeria electrònica::Microelectrònica::Circuits integrats -Integrated circuits -- Very large scale integration -Boolean decomposition -AIG -KL-cuts -Local optimization -Circuits integrats a molt gran escala
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Tipo de documento:	Artículo - Versión presentada Objeto de conferencia
Editor:	Association for Computing Machinery (ACM)
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Machado, Lucas; Roca Pérez, Antoni; Cortadella, Jordi

Machado, Lucas; Cortadella, Jordi

Machado, Lucas; Roca Pérez, Antoni; Cortadella, Jordi

Machado, Lucas; Roca Pérez, Antoni; Cortadella, Jordi

Machado, Lucas; Dal Bem, Vinicius; Moll Echeto, Francisco de Borja; Gómez Fernández, Sergio; Ribas, Renato P.; Reis, André Inacio

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