Scalable Boolean Methods in a Modern Synthesis Flow
Eleonora Testa1,2, Luca Amarú1, Mathias Soeken2, Alan Mishchenko3, Patrick Vuillod1, Jiong Luo1, Christopher Casares1, Pierre-Emmanuel Gaillardon4 and Giovanni De Micheli2
1Synopsys Inc., Design Group, Sunnyvale, California, USA
2Integrated Systems Laboratory, EPFL, Switzerland
3Department of EECS, UC Berkeley, Berkeley, California, USA
4LNIS, University of Utah, Salt Lake City, Utah, USA
ABSTRACT
With the continuous push to improve Quality of Results (QoR) in EDA, Boolean methods in logic synthesis have been recently drawing the attention of researchers. Boolean methods achieve better QoR than algebraic methods but require higher computational cost. In this paper, we introduce the Scalable Boolean Method (SBM) framework. The SBM consists of 4 optimization engines designed to be scalable in a modern synthesis flow. The first presented engine is a generalized resubstitution framework based on computing, and implementing, the Boolean difference between two nodes. The second consists of a gradientbased AIG optimization, while the third one is based on heterogeneous elimination for kerneling. The last proposed engine is a revisiting of maximum set of permissible functions computation with BDDs. Altogether, the SBM framework enables significant synthesis results. We improve 12 of the best known area results in the EPFL synthesis competition. Embedded in a commercial EDA flow, the new Boolean methods enable -2.20% combinational area savings and -5.99% total negative slack reduction, after physical implementation, at contained runtime cost.
