Improving symbolic reachability analysis by means of activityprofiles

Symbolic techniques have undergone major improvements in the last few gears. Nevertheless, applications are still limited by memory size and time constraints. As a consequence, extending their applicability to larger and real circuits is still a key issue. Within this framework, we introduce "activity profiles" as a novel technique to characterize finite state machines described by their transition relation. In our methodology, a "learning phase" is used to collect activity measures. They are gathered, in an inexpensive way, for each binary decision diagram node of the transition relation. They indicate the activity the node has been involved in, as an estimate of its correlation with space and/or time costs. The above information can be used for several purposes. In particular, we present an application of activity profiles in the field of reachability analysis, to enhance memory and time performance of traversals. More specifically, we use transition relation subsetting in order to traverse the state transition graph in a nonpurely breadth-first, guided and multistep fashion. Comparisons with other state-of-the-art approaches show that our sequence of partial traversals produces "best-ever" results for all the large benchmarks analyzed.