Decision diagram optimization using copy properties

In this paper, we propose an approach to the reduction of sizes of multi-terminal binary decision diagrams (MTBDDs) by using the copy properties of discrete functions. The underlying principles come from copy theory of discrete signals considered previously. We propose two modifications of MTBDDs, called copy DDs (CDDs) and half copy DDs (HCDDs), using the corresponding copy operations from copy theory. Functions having different types of copy properties can be efficiently represented by the proposed Copy DDs. Examples are Walsh and Reed-Muller functions as well as different binary codes.