An efficient design space exploration methodology for multiprocessor SoC architectures based on response surface methods

Multi-processor system on-chip (MPSoC) architectures are currently designed by using a platform-based approach. In this approach, a wide range of platform parameters must be tuned to find the best trade-offs in terms of the selected figures of merit (such as energy, delay and area). This optimization phase is called design space exploration (DSE) and it generally consists of a multi-objective optimization (MOO) problem. The design space for an MPSoC architecture is too large to be evaluated comprehensively. So far, several heuristic techniques have been proposed to address the MOO problem for MPSoC, but they are characterized by low efficiency to identify the Pareto front. In this paper, an efficient DSE methodology is proposed leveraging traditional Design of Experiments (DoE) and response surface modeling (RSM) techniques. In particular, the DoE phase generates an initial plan of experiments used to create a coarse view of the target design space; a set of RSM techniques are then used to refine the exploration. This process is iteratively repeated until the target criterion (e.g. number of simulations) is satisfied. A set of experimental results are reported to trade-off accuracy and efficiency of the proposed techniques with actual workloads.