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  • Dynamic Minimization of Bi-Kronecker Functional Decision Diagrams

    Author(s)
    Huang, X
    Che, H
    Fang, L
    Chen, Q
    Guan, Q
    Deng, Y
    Su, K
    Griffith University Author(s)
    Su, Kaile
    Year published
    2020
    Metadata
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    Abstract
    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 ...
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    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.
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    Conference Title
    ICCAD '20: Proceedings of the 39th International Conference on Computer-Aided Design
    DOI
    https://doi.org/10.1145/3400302.3415618
    Subject
    Theory of computation
    Publication URI
    http://hdl.handle.net/10072/401688
    Collection
    • Conference outputs

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