The extended partitioning problem: hardware/software mapping and implementation-bin selection

The extended partitioning problem is the joint problem of mapping nodes in a precedence graph to hardware or software, and within each mapping, selecting an appropriate implementation for each node. The end-goal is to minimize the hardware area, subject to architectural and performance constraints. This is an NP-complete problem; we present an efficient heuristic called MIBS to solve it. The MIBS (Mapping and Implementation-Bin Selection) algorithm solves the extended partitioning problem by decomposing it into an iterative process consisting of two steps: mapping and implementation-bin selection (IBS). The GCLP (Global Criticality/Local Phase-driven) algorithm computes a mapping by using an adaptive optimization objective at each iteration. This objective is selected on the basis of a global time criticality measure and local optimality measures. The IBS algorithm solves the implementation-bin selection problem. It uses a bin sensitivity measure which correlates the implementation bin motion with the overall hardware area reduction, to determine the implementation bin of a node for a given mapping. Experimental results indicate that the added dimension of design flexibility (offered by implementation bins) can be used effectively in partitioning to reduce the overall area. The MIBS algorithm has O(|N| ³) complexity, with a solution quality comparable to that of ILP (integer linear programming)