Value prediction in hls allocation problems using intellectual properties

A value approximation-based global search algorithm is suggested to solve resource-constrained allocation in high level synthesis problems. Value approximation is preferred, because it can start by using expert heuristics, can estimate the global structure of the search problem, and can optimize heuristics. We are concerned by those allocation problems that have hidden global structure that value approximation may unravel. The value approximation applied here computes the cost of the actual solution and estimates the cost of the solution that could be achieved upon performing a global search on the hidden structure starting from the actual solution. We transcribed the allocation problem into a special form of weighted CNF formulae to suit our approach. We also extended the formalism to pipeline operations. Comparisons are made with expert heuristics. Scaling of computation time and performance are compared.