Minimization of memory size for heterogeneous MDDs

In this paper, we propose exact and heuristic algorithms for minimizing the memory size for heterogeneous Multivalued Decision Diagrams (MDDs). In a heterogeneous MDD, each multi-valued variable can take a different domain. To represent a binary logic function using a heterogeneous MDD, we partition the binary variables into groups, and treat the groups as multi-valued variables. Therefore, the memory size of a heterogeneous MDD depends on the partition of the binary variables. Our experimental results show that heterogeneous MDDs require smaller memory size than Reduced Ordered Binary Decision Diagrams (ROBDDs) and Free BDDs (FBDDs).