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The increasing complexity of electric/electronic architectures (EEA) in the automotive domain raised the necessity of model-based development processes for the design of such heterogeneous systems, which combine different engineering principles with different viewpoints. High-level simulation is a great means to evaluate the EEA in the concept phase of the design, since it reduces costly real-world experiments. However, model-based EEA design and analysis as well as its simulation are often separate processes in the development lifecycle. In this paper, we present a novel approach that extends stateof-the-art model-based systems engineering principles of EEA by a behavior specification reusing library components. The specification is seamlessly integrated in the development process of a single source EEA model. Therewith, the starting point is the abstract logical function architecture of the EEA. Based on this single source EEA model we synthesize a unified high-level simulation model, which is capable of linking the behavioral model with lower level implementation details of other domains, e.g. the network communication of the underlying hardware topology. This cross-layer simulation enables an early but holistic system's behavior analysis of the dynamic changes which typically depend on the scenarios applied. Moreover, the integrated approach enables the potential to feedback the simulation results into suitable EEA metrics and benchmarks for further analysis and optimization as well as the seamless traceability of the behavioral specification to requirements and other abstraction layers. A driver assistance system use case demonstrates the proof-ofconcept and the benefits of our methodology.

Parallel architectures are nowadays not only confined to the domain of high performance computing, they are also increasingly used in embedded time-critical systems. The ARGO H2020 project 1 provides a programming paradigm and associated tool flow to exploit the full potential of architectures in terms of development productivity, time-to-market, exploitation of the platform computing power and guaranteed real-time performance. In this paper we give an overview of the objectives of ARGO and explore the challenges introduced by our approach.

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