Compact DSOP and partial DSOP Forms

TL;DR

This paper introduces a new heuristic approach for minimizing Disjoint Sum-of-Products (DSOP) and partial DSOP forms, which are crucial in digital circuit synthesis, demonstrating superior results over existing methods.

Contribution

The paper develops a novel heuristic based on product weights for efficient partial DSOP minimization, improving over previous algorithms and handling NP-hardness.

Findings

The heuristic outperforms existing tools on benchmark functions.

The method consistently produces smaller DSOP and partial DSOP forms.

Experimental results validate the effectiveness of the proposed approach.

Abstract

Given a Boolean function f on n variables, a Disjoint Sum-of-Products (DSOP) of f is a set of products (ANDs) of subsets of literals whose sum (OR) equals f, such that no two products cover the same minterm of f. DSOP forms are a special instance of partial DSOPs, i.e. the general case where a subset of minterms must be covered exactly once and the other minterms (typically corresponding to don't care conditions of $f$) can be covered any number of times. We discuss finding DSOPs and partial DSOP with a minimal number of products, a problem theoretically connected with various properties of Boolean functions and practically relevant in the synthesis of digital circuits. Finding an absolute minimum is hard, in fact we prove that the problem of absolute minimization of partial DSOPs is NP-hard. Therefore it is crucial to devise a polynomial time heuristic that compares favorably with the known minimization tools. To this end we develop a further piece of theory starting from the definition of the weight of a product p as a functions of the number of fragments induced on other cubes by the selection of p, and show how product weights can be exploited for building a class of minimization heuristics for DSOP and partial DSOP synthesis. A set of experiments conducted on major benchmark functions show that our method, with a family of variants, always generates better results than the ones of previous heuristics, including the method based on a BDD representation of f.