Abstract — Early design space exploration (DSE) is a key ingredient in system-level design of MPSoC-based embedded systems. The state of the art in this field typically still explores systems under a single, fixed application workload. In reality, however, the applications are concurrently executing and contending for system resources in such systems. As a result, the intensity and nature of application demands can change dramatically over time. This paper therefore introduces the concept of workload scenarios in the DSE process, capturing dynamic behavior both within and between applications. More specifically, we present and evaluate a novel, scenario-based DSE approach based on a coevolutionary genetic algorithm. I.

Abstract: This paper presents a novel approach to efficiently perform early system level design space exploration (DSE) of MultiProcessor System-on-Chip (MPSoC) based embedded systems. By modeling dynamic multi-application workloads using application scenarios, optimal designs can be quickly identified using a combination of a scenario-based DSE and a feature selection algorithm. The feature selection algo-rithm identifies a representative subset of scenarios, which is used to predict the fitness of the MPSoC design instances in the genetic algorithm of the scenario-based DSE. Results show that our scenario-based DSE provides a tradeoff between the speed and accuracy of the early DSE. 1

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Abstract—Modern embedded systems are becoming increasingly multi-functional. The dynamism in multi-functional 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-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 multiobjective genetic algorithm (GA) to identifying the mapping that has the best average quality for all the application scenarios in the workload. In order to keep the exploration of the scenariobased 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 multi-functional embedded systems.