Scalable Logic Rewriting Using Don't Cares

Logic rewriting is a powerful optimization technique that replaces small sections of a Boolean network with better implementations. Typically, exact synthesis is used to compute optimum replacement on-the-fly, with possible support for Boolean don't cares. However, exact synthesis is computationally expen-sive, rendering it impractical in industrial tools. For this reason, optimum structures are typically pre-computed and stored in a database, commonly limited to 4-inputs. Nevertheless, this method does not support the use of don't cares. In this paper, we propose a technique to enable the usage of don't cares in pre-computed databases. We show how to process the database and perform Boolean matching with Boolean don't cares, with negligible run time overhead. Logic rewriting techniques are typically very effective at optimizing majority-inverter graphs (MIGs). In the experiments, we show that the usage of don't cares in logic rewriting on MIGs offers an average size improvement of 4.31 % and up to 14.32% compared to state-of-the-art synthesis flow.