Exploring time/resource trade-offs by solving dual scheduling problems with the ant colony optimization

Design space exploration during high-level synthesis is often conducted through ad hoc probing of the solution space using some scheduling algorithm. This is not only time consuming but also very dependent on designer's experience. We propose a novel design exploration method that exploits the duality of time- and resource-constrained scheduling problems. Our exploration automatically constructs a time/area tradeoff curve in a fast, effective manner. It is a general approach and can be combined with any high-quality scheduling algorithm. In our work, we use the max-min ant colony optimization technique to solve both time- and resource-constrained scheduling problems. Our algorithm provides significant solution-quality savings (average 17.3% reduction of resource counts) with similar runtime compared to using force-directed scheduling exhaustively at every time step. It also scales well across a comprehensive benchmark suite constructed with classic and real-life samples.