Scalable don't-care-based logic optimization and resynthesis

Abstract

We describe an optimization method for combinational and sequential logic networks, with emphasis on scalability. The proposed resynthesis (a) is capable of substantial logic restructuring, (b) is customizable to solve a variety of optimization tasks, and (c) has reasonable runtime on industrial designs. The approach uses don't-cares computed for a window surrounding a node and can take into account external don't-cares (e.g., unreachable states). It uses a SAT solver for all aspects of Boolean manipulation: computing don't-cares for a node in the window, and deriving a new Boolean function of the node after resubstitution. Experimental results on 6-input LUT networks after a high effort synthesis show substantial reductions in area and delay. When applied to 20 large academic benchmarks, the LUT counts and logic levels are reduced by 45.0% and 12.2%, respectively. The longest runtime for synthesis and mapping is about two minutes. When applied to a set of 14 industrial benchmarks ranging up to 83K 6-LUTs, the LUT counts and logic levels are reduced by 11.8% and 16.5%, respectively. The longest runtime is about 30 minutes.