Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Efficient implementation of a BDD package
DAC '90 Proceedings of the 27th ACM/IEEE Design Automation Conference
Who are the variables in your neighborhood
ICCAD '95 Proceedings of the 1995 IEEE/ACM international conference on Computer-aided design
Improving the Variable Ordering of OBDDs Is NP-Complete
IEEE Transactions on Computers
On the effect of local changes in the variable ordering of ordered decision diagrams
Information Processing Letters
Interleaving based variable ordering methods for ordered binary decision diagrams
ICCAD '93 Proceedings of the 1993 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
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
Combination of Lower Bounds in Exact BDD Minimization
DATE '03 Proceedings of the conference on Design, Automation and Test in Europe - Volume 1
Fast exact minimization of BDD's
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Using lower bounds during dynamic BDD minimization
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Distributed dynamic BDD reordering
Proceedings of the 43rd annual Design Automation Conference
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In this paper we present new lower bounds on BDD size. These lower bounds are derived from more general lower bounds that recently were given in the context of exact BDD minimization. The results presented in this paper are twofold: first, we gain deeper insight by looking at the theory behind the new lower bounds. Examples lead to a better understanding, showing that the new lower bounds are effective in situations where this is not the case for previous lower bounds and vice versa. Following the constraints in practice, we then compromise between runtime and quality of the lower bounds. Finally, a clever combination of old and new lower bounds results in a final lower bound, yielding a significant improvement. Experimental results show the efficiency of our approach.