  Discrete integration is a fundamental problem in computer science that
concerns the computation of discrete sums over exponentially large sets.
Despite intense interest from researchers for over three decades, the design of
scalable techniques for computing estimates with rigorous guarantees for
discrete integration remains the holy grail. The key contribution of this work
addresses this scalability challenge via an efficient reduction of discrete
integration to model counting. The proposed reduction is achieved via a
significant increase in the dimensionality that, contrary to conventional
wisdom, leads to solving an instance of the relatively simpler problem of model
counting.
  Building on the promising approach proposed by Chakraborty et al, our work
overcomes the key weakness of their approach: a restriction to dyadic weights.
We augment our proposed reduction, called DeWeight, with a state of the art
efficient approximate model counter and perform detailed empirical analysis
over benchmarks arising from neural network verification domains, an emerging
application area of critical importance. DeWeight, to the best of our
knowledge, is the first technique to compute estimates with provable guarantees
for this class of benchmarks.
