Average path length of binary decision diagrams

The traditional problem in binary decision diagrams (BDDs) has been to minimize the number of nodes since this reduces the memory needed to store the BDD. Recently, a new problem has emerged: minimizing the average path length (APL). APL is a measure of the time needed to evaluate the function by applying a sequence of variable values. It is of special significance when BDDs are used in simulation and design verification. A main result of this paper is that the APL for benchmark functions is typically much smaller than for random functions. That is, for the set of all functions, we show that the average APL is close to the maximum path length, whereas benchmark functions show a remarkably small APL. Surprisingly, however, typical functions do not achieve the absolute maximum APL. We show that the parity functions are unique in having that distinction. We show that the APL of a BDD can vary considerably with variable ordering. We derive the APL for various functions, including the AND, OR, threshold, Achilles´ heel, and certain arithmetic functions. We show that the unate cascade functions uniquely achieve the absolute minimum APL.