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@inproceedings{GSRM21,
  title={Engineering an Efficient Boolean Functional Synthesis Engine},
  author={
    Golia, Priyanka and Slivovsky, Friedrich and Roy, Subhajit and Meel, Kuldeep
    S.
  },
  booktitle=ICCAD,
  year={2021},
  month=nov,
  bib2html_dl_pdf={../Papers/iccad21.pdf},
  bib2html_rescat={Synthesis},
  note={Best Paper Award Nomination},
  bib2html_pubtype={Refereed Conference,Award Winner},
  abstract={
    Given a Boolean specification between a set of inputs and outputs, the
    problem of Boolean functional synthesis is to synthesise each output as a
    function of inputs such that the specification is met. Although the past few
    years have witnessed intense algorithmic development, accomplishing
    scalability remains the holy grail. The state of-the-art approach combines
    machine learning and automated reasoning to synthesise Boolean functions
    efficiently. In this paper, we propose four algorithmic improvements for a
    data-driven framework for functional synthesis: using a dependency-driven
    multi-classifier to learn candidate function, extracting uniquely defined
    functions by interpolation, variables retention, and using lexicographic
    MaxSAT to repair candidates.
    We integrate these improvements into the state-of-the-art frame- work,
    called Manthan. The resulting framework, Manthan2, shows significantly
    improved runtime performance compared to Manthan. In an extensive
    experimental evaluation on 609 benchmarks, Manthan2 is able to synthesise a
    Boolean function vector for 509 instances compared to 356 instances solved
    by Manthan - an increment of 153 instances over the state of-the-art. To put
    this into perspective, Manthan improved on the prior state of the art by
    only 76 instances.
  },
  area={ar},
}