HypAp: A Hypervolume-Based Approach for Refining the Design of Embedded Systems

Designing complex embedded systems requires simultaneous optimization of multiple system performance metrics that can be addressed by applying Pareto-based multiobjective optimization techniques. At the end of this type of optimization process, designers always face Pareto fronts (PFs) including a large number of near-optimal solutions from which selecting the most proper system implementation is potentially infeasible. In this letter, for the first time, we present HypAp, a hypervolume-based automated approach to systematically help designers efficiently choose their preferred solutions after the optimization process. HypAp is a two-stage approach relying on clustering Pareto optimal solutions and then finding a subset of solutions that maximizes the hypervolume by using a genetic algorithm. The performance of HypAp is evaluated through applying HypAp to the PF by the case study of mapping applications on network-on-chip-based heterogeneous MPSoC.