This paper considers methods to design multiple-output networks based on decision diagrams (DDs). TDM (time-division multiplexing) systems transmit several signals on a single line. These methods reduce: 1) hardware; 2) logic levels; and 3) pins. In the TDM realizations, we consider three types of DDs: shared binary decision digrams (SBDDs), shared multiple-valued decision diagrams (SMDDs), and shared multi-terminal multiple-valued decision diagrams (SMTMDDs). In the network, each non-terminal node of a DD is realized by a multiplexer (MUX). We propose heuristic algorithms to derive SMTMDDs from SBDDs. We compare the number of non-terminal nodes in SBDDs, SMDDs, and SMTMDDs. For nrm n, log n, and for many other benchmark functions, SMTMDD-based realizations are more economical than other ones, where nrm n is a (2n)-input (n1)-output function computing (X2+Y2)+0.5, log n is an n-input n-output function computing (2n1)log(x1)/nlog2, and a denotes the largest integer not greater than a.

This paper proposes a method to construct smaller binary decision diagrams for characteristic functions (BDDs for CFs). A BDD for CF represents an n-input m-output function, and evaluates all the outputs in O(n+m) time. We derive an upper bound on the number of nodes of the BDD for CF of n-bit adders (adrn). We also compare complexities of BDDs for CFs with those of shared binary decision diagrams (SBDDs) and multi-terminal binary decision diagrams (MTBDDs). Our experimental results show: 1) BDDs for CFs are usually much smaller than MTBDDs; 2) for adrn and for some benchmark circuits, BDDs for CFs are the smallest among the three types of BDDs; and 3) the proposed method often produces smaller BDDs for CFs than an existing method.

In this paper, we propose a method to minimize multiple-valued decision diagrams (MDDs) for multiple-output functions. We consider the following: (1) a heuristic for encoding the 2-valued inputs; and (2) a heuristic for ordering the multiple-valued input variables based on sampling, where each sample is a group of outputs. We first generate a 4-valued input 2-valued multiple-output function from the given 2-valued input 2-valued functions. Then, we construct an MDD for each sample and find a good variable ordering. Finally, we generate a variable ordering from the orderings of MDDs representing the samples, and minimize the entire MDDs. Experimental results show that the proposed method is much faster, and for many benchmark functions, it produces MDDs with fewer nodes than sifting. Especially, the proposed method generates much smaller MDDs in a short time for benchmark functions when several 2-valued input variables are grouped to form multiple-valued variables.

This paper describes a method to represent m output functions using shared multi-terminal binary decision diagrams (SMTBDDs). The SMTBDD(k) consists of multi-terminal binary decision diagrams (MTBDDs), where each MTBDD represents k output functions. An SMTBDD(k) is the generalization of shared binary decision diagrams (SBDDs) and MTBDDs: for k=1, it is an SBDD, and for k=m, it is an MTBDD. The size of a BDD is the total number of nodes. The features of SMTBDD(k)s are: 1) they are often smaller than SBDDs or MTBDDs; and 2) they evaluate k outputs simultaneously. We also propose an algorithm for grouping output functions to reduce the size of SMTBDD(k)s. Experimental results show the compactness of SMTBDD(k)s. An SMTBDDmin denotes the smaller SMTBDD which is either an SMTBDD(2) or an SMTBDD(3) with fewer nodes. The average relative sizes for SBDDs, MTBDDs, and SMTBDDs are 1. 00, 152. 73, and 0. 80, respectively.