Fitness Prediction Techniques for Scenario-Based Design Space Exploration

Abstract

Modern embedded systems are becoming increasingly multifunctional. The dynamism in multifunctional embedded systems manifests itself with more dynamic applications and the presence of multiple applications executing on a single embedded system. This dynamism in the application workload must be taken into account during the early system-level design space exploration (DSE) of multiprocessor system-on-a-chip (MPSoC)-based embedded systems. Scenario-based DSE utilizes the concept of application scenarios to search for optimal mappings of a multi-application workload onto an MPSoC. The scenario-based DSE uses a multi-objective genetic algorithm (GA) to identifying the mapping with the best average quality for all the application scenarios in the workload. In order to keep the exploration of the scenario-based DSE efficient, fitness prediction is used to obtain the quality of a mapping. This fitness prediction is performed using a representative subset of application scenarios that is obtained using co-exploration of the scenario subset space. In this paper, multiple fitness prediction techniques are presented: stochastic, deterministic, and a hybrid combination. Results show that, for our test cases, accurate fitness prediction is already provided for subsets containing only 1-4% of the application scenarios. Larger subsets will obtain a similar accuracy, but the DSE will require more time to identify promising mappings that meet the requirements of multifunctional embedded systems.