Dynamic Minimization of Bi-Kronecker Functional Decision Diagrams

A recently proposed canonical representation called Bi-Kronecker Functional Decision Diagrams (BKFDDs) utilizes the classical decompositions (the Shannon and Davio decompositions) and their biconditional variants, and hence can be seen as a generalization of some existing decision diagrams: BDDs, FD...

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Vydáno v:Digest of technical papers - IEEE/ACM International Conference on Computer-Aided Design s. 1 - 9
Hlavní autoři: Huang, Xuanxiang, Che, Haipeng, Fang, Liangda, Chen, Qingliang, Guan, Quanlong, Deng, Yuhui, Su, Kaile
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: Association on Computer Machinery 02.11.2020
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ISSN:1558-2434
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Shrnutí:A recently proposed canonical representation called Bi-Kronecker Functional Decision Diagrams (BKFDDs) utilizes the classical decompositions (the Shannon and Davio decompositions) and their biconditional variants, and hence can be seen as a generalization of some existing decision diagrams: BDDs, FDDs, KFDDs and BBDDs. However, the size of BKFDDs for a Boolean function is very sensitive to variable orders with decomposition types (ODTs). Therefore, identifying a good ODT is of paramount importance for BKFDDs. In this paper, we propose four dynamic minimization algorithms for BKFDDs, which encapsulate smart strategies to search for a good ODT in a dynamic way. The experiments have been carried out on four influential benchmarks: ISCAS89, MCNC, ITC99 and EPFL, and the experimental results show that the proposed group sifting algorithms for BKFDDs are very effective and can produce BKFDDs with smaller size than state-of-the-art packages of DDs.
ISSN:1558-2434
DOI:10.1145/3400302.3415618