A Heterogeneous Multi-valued Decision Diagram Machine for Encoded Characteristic Function for Non-zero Outputs
Hiroki Nakahara, Tsutomu Sasao and Munehiro Matsuura
A heterogeneous multi-valued decision diagram (HMDD) may have nodes with different numbers of variables. By partitioning the input variables into optimal disjoint sets, the HMDDs evaluate the function faster than BDDs with the same amount of memory. A HMDD for encoded characteristic function for non-zero outputs (ECFN) represents a multi-output logic function efficiently. This paper shows an HMDD for an ECFN machine. First, we introduce the HMDD for ECFN. Then, we show an architecture for the HMDD for ECFN machine. Also, by experiment, we show that compared with the Intel’s Core i5 processor running at 2.4 GHz, as for the speed, the HMDD for ECFN machine is 1.40-4.27 times faster, and as for the power-delay product, it is 15.1- 46.4 times smaller.
Keywords: Decision diagram machine, heterogeneous multi-valued decision diagram