Nonlinear Sifting of De ision Diagrams

Linear Sifting of Decision Diagrams yChristoph

We propose a new algorithm, called linear sifting, for the optimization of decision diagrams that combines the ee-ciency of sifting and the power of linear transformations. We show that the new algorithm is applicable to large examples , and that in many cases it leads to substantially more compact diagrams when compared to simple variable reordering. We show in what sense linear transformation...

Augmented Sifting of Multiple-Valued Decision Diagrams

Discrete functions are now commonly represented by binary (BDD) and multiple-valued (MDD) decision diagrams. Sifting is an effective heuristic technique which applies adjacent variable interchanges to find a good variable ordering to reduce the size of a BDD or MDD. Linear sifting is an extension of BDD sifting where XOR operations involving adjacent variable pairs augment adjacent variable int...

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Abstra t. Bounded rea hability or model he king is widely believed to work poorly when using de ision diagrams instead of SAT pro edures. Re ent resear h suggests this to be untrue with regards to syn hronous systems, parti ularly digital ir uits. This paper shows that the belief is also a myth for asyn hronous systems, su h as models spe i ed by Petri nets. We propose Bounded Saturation, a new...

Lower Bound Sifting for MDDs

Decision Diagrams (DDs) are a data structure for the representation and manipulation of discrete logic functions often applied in VLSI CAD. Common DDs to represent Boolean functions are Binary Decision Diagrams (BDDs). Multiple-valued logic functions can be represented by Multiple-valued Decision Diagrams (MDDs). The efficiency of a DD representation strongly depends on the variable ordering; t...

Sizes of de ision tables and de ision trees