  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 efficiently synthesise Boolean functions. 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
implement these improvements in the state-of-the-art framework, called Manthan.
The proposed framework is called Manthan2. 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.
