Large-scale SOP minimization using decomposition and functional properties

In some cases, minimum sum-of-products (SOP) expressions of Boolean functions can be derived by detecting decomposition and observing the functional properties such as unateness, instead of applying the classical minimization algorithms. This paper presents a systematic study of such situations and develops a divide-and-conquer algorithm for SOP minimization, which can dramatically reduce the computational effort, without sacrificing the minimality of the solutions. The algorithm is used as a preprocessor to a general-purpose exact or heuristic minimizer, such as ESPRESSO. The experimental results show significant improvements in runtime. The exact solutions for some large MCNC benchmark functions are reported for the first time.