Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Model checking and abstraction
ACM Transactions on Programming Languages and Systems (TOPLAS)
BDD variable ordering for interacting finite state machines
DAC '94 Proceedings of the 31st annual Design Automation Conference
Who are the variables in your neighborhood
ICCAD '95 Proceedings of the 1995 IEEE/ACM international conference on Computer-aided design
Dynamic variable ordering for ordered binary decision diagrams
ICCAD '93 Proceedings of the 1993 IEEE/ACM international conference on Computer-aided design
Sampling schemes for computing OBDD variable orderings
Proceedings of the 1998 IEEE/ACM international conference on Computer-aided design
Symbolic Model Checking
Equivalence checking using abstract BDDs
ICCD '97 Proceedings of the 1997 International Conference on Computer Design (ICCD '97)
On variable ordering of binary decision diagrams for the application of multi-level logic synthesis
EURO-DAC '91 Proceedings of the conference on European design automation
An efficient estimation of the ROBDD's complexity
Integration, the VLSI Journal
Constraint and variable ordering heuristics for compiling configuration problems
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
An efficient estimation of the ROBDD's complexity
Integration, the VLSI Journal
Model-based variable and transition orderings for efficient symbolic model checking
FM'06 Proceedings of the 14th international conference on Formal Methods
New metrics for static variable ordering in decision diagrams
TACAS'06 Proceedings of the 12th international conference on Tools and Algorithms for the Construction and Analysis of Systems
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Variable ordering for BDDs has been extensively investigated. Recently, sampling based ordering techniques have been proposed to overcome problems with structure based static ordering methods and sifting based dynamic reordering techniques. However, existing sampling techniques can lead to an unacceptably large deviation in the size of the final BDD. In this paper, we propose a new sampling technique based on abstract BDDs (aBDDs) that does not suffer from this problem. This new technique, easy to implement and automate, consistently creates high quality variable orderings for both combinational as well as sequential functions. Experimental results show that for many applications our approach is significantly superior to existing techniques.