Advanced BDD Optimization
Autor Rudiger Ebendt, Görschwin Fey, Rolf Drechsleren Limba Engleză Hardback – 23 aug 2005
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Specificații
ISBN-13: 9780387254531
ISBN-10: 0387254536
Pagini: 222
Ilustrații: X, 222 p.
Dimensiuni: 156 x 232 x 18 mm
Greutate: 0.52 kg
Ediția:2005
Editura: Springer Us
Colecția Springer
Locul publicării:New York, NY, United States
ISBN-10: 0387254536
Pagini: 222
Ilustrații: X, 222 p.
Dimensiuni: 156 x 232 x 18 mm
Greutate: 0.52 kg
Ediția:2005
Editura: Springer Us
Colecția Springer
Locul publicării:New York, NY, United States
Public țintă
ResearchCuprins
Preface. 1. Introduction. 2. Preliminaries. 2.1. Notation. 2.2. Boolean Functions. 2.3. Decomposition of Boolean Functions. 2.4. Reduced Ordered Binary Decision Diagrams.- 3. Exact node Minimization. 3.1. Branch and Bound Algorithm. 3.2. A*-Based Optimization. 3.3. Summary.- 4. Heuristic node Minimization. 4.1. Efficient Dynamic Minimization. 4.2. Improved Lower Bounds for Dynamic Reordering. 4.3. Efficient Forms of Improved Lower Bounds. 4.4. Combination of Improved Lower Bounds with Classical Bounds. 4.5. Experimental Results. 4.6. Summary.- 5. Path Minimization. 5.1. Minimization of Number of Paths. 5.2. Minimization of Expected Path Length. 5.3. Minimization of Average Path Length. 5.4. Summary.- 6. Relation between SAT and BDDS. 6.1. Davis-Putnam Procedure. 6.2. On the Relation between DP Procedure and BDDs. 6.3. Dynamic Variable Ordering Strategy for DP Procedure. 6.4. Experimental Results. 6.5. Summary.- 7. Final Remarks. References. Index.
Caracteristici
BDD and SAT are major concepts in VLSI CAD New objective functions for design space exploration require new algorithms for BDD optimization Latest trend: fusion of the concepts BDD and SAT Major impulses come from Artificial Intelligence (AI) Unifying view, transfers the latest theoretical insights into practical applications Includes supplementary material: sn.pub/extras