Quasi-Exact BDD Minimization Using Relaxed Best-First Search
ISVLSI '05 Proceedings of the IEEE Computer Society Annual Symposium on VLSI: New Frontiers in VLSI Design
Weighted A∗ search -- unifying view and application
Artificial Intelligence
A framework for quasi-exact optimization using relaxed best-first search
KI'06 Proceedings of the 29th annual German conference on Artificial intelligence
A microcanonical optimization algorithm for BDD minimization problem
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Dynamic segregative genetic algorithm for optimizing the variable ordering of ROBDDs
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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Reduced-ordered binary decision diagrams (BDDs) are a data structure for efficient representation and manipulation of Boolean functions. They are frequently used in logic synthesis. The size of BDDs depends on a chosen variable ordering, i.e., the size may vary from linear to exponential, and the problem of improving the variable ordering is known to be NP-complete. In this paper, a new exact BDD minimization algorithm called Astute is presented. Here, ordered best-first search, i.e., the A* algorithm, is combined with a classical branch-and-bound (B&B) algorithm. A* operates on a state space large parts of which are pruned by a best-first strategy expanding only the most promising states. Combining A* with B&B allows to avoid unnecessary computations and to save memory. Experimental results demonstrate the efficiency of our approach.