Abstract-This paper proposes a modeling approach to capture the mapping of an application on a platform. The approach is based on Scenario-Aware Dataflow (SADF) models. In contrast to the related work, we express the complete design-space in a single formal SADF model. This allows us to have a compact and explorable state-space linked with an executable model capable of symbolically analyzing different mappings for their timing behavior. We can model different bindings for application tasks, different static-orders schedules for tasks bound in shared resources, as well as naturally capturing resource claiming/unclaiming using SADF semantics. Moreover, by using the inherent properties of dataflow graphs and the dynamic behavior of a Finite-State Machine, we can model different levels of pipelining, such as full application pipelining and interleaved pipelining of consecutive executions of the application. The size of the model is independent of the number of executions of the application. Since we are able to capture all this behavior in a single SADF model we can use available dataflow analysis, such as worst-case and best-case throughput and deadlock-freedom checking. Furthermore, since the model captures the design-space independently of the analysis technique, one can use different exploration approaches to analyze different sets of requirements.
I. INTRODUCTIONIn a traditional approach to system design, the first exploration phase focuses on iterating over different design alternatives to find the best solution that satisfies a given set of requirements and constraints. For example, real-time embedded systems have to perform under strict timing guarantees, or high-performance production systems focus on maximizing resource utilization to achieve maximal throughput. However, exploring different design alternatives is highly dependent on an efficient binding and scheduling of the application in order to analyze the performance of each design. Therefore, several approaches have been proposed to model and explore different mapping options. However, current modeling approaches for the binding and scheduling are not transparent, use different models for application and platform or rely on later transformations to different models and formalisms to solve the actual state-space exploration problem.In this paper 1 we present a modeling approach based on Scenario-Aware Dataflow (SADF) that provides a single, compact and formal model of the system which reflects the complete state-space for all binding and scheduling options of an application to be mapped on a given platform. Our approach allows for the modeling of both application and platform constraints, keeping all deterministic behavior of the application within static dataflow graphs and model explicitly the different choices of scheduling and binding decisions in a Finite-State Machine. The approach uses a rich formalism capable of capturing different applications or different levels of application pipelining. The resulting model uses an underlying symbolic exec...