Accelerated SAT-based scheduling of control/data flow graphs

In this paper we present a satisfiability-based approach to the scheduling problem in high-level synthesis. We formulate the resource constrained scheduling as a satisfiability (SAT) problem. We present experimental results on the performance of the state-of-the-art SAT solver, Chaff, and demonstrate techniques to reduce the SAT problem size by applying bounding techniques on the scheduling problem. In addition, we demonstrate the use of some transformations on control data flow graphs such that the same lower bound techniques can operate on them as well. Our experiments show that Chaff is able to outperform the Integer Linear Program (ILP) solver CPLEX in terms of CPU time by as much as 59 fold. Finally, we conclude that the satisfiability-based approach is a promising alternative for obtaining optimal solutions to NP-Complete scheduling problem instances.