An extensible modeling methodology for embedded and cyber-physical system design. (August 2016)

• Record Type: Journal Article
• Authors: Attarzadeh-Niaki, Seyed-Hosein; Sander, Ingo
• Is Part Of: Simulation. Volume 92:Number 8(2016:Aug.)
• Page Start: 771
• Page End: 794
• Publication Date: 2016-08
• Subjects: Cyber-physical systems -- formal modeling -- SystemC -- model introspection -- models of computation; Computer simulation -- Periodicals; 003.3
• DOI: 10.1177/0037549716659753
• Languages: English
• ISSNs: 0037-5497

Abstract

Abstract models are important tools to manage the increasing complexity of system design. The choice of a modeling language for constructing models governs what types of systems can be modeled, and which subsequent design activities can be performed. This is especially true for the area of embedded electronic and cyber-physical system design, which poses several challenging requirements of modeling and design methodologies. This article argues that theFor malSy stemDe sign (ForSyDe) methodology with the necessary presented extensions fulfills these requirements, and thus qualifies for the design of tomorrow's systems. Based on the theory of models of computation and the concept of process constructors, heterogeneous models are captured in ForSyDe with formal semantics. A refined layer of the formalism is introduced to make its denotational-style semantics easy to implement on top of commonly used imperative languages, and an open-source realization on top of the IEEE standard language SystemC is presented. The introspection mechanism is introduced to automatically export an intermediate representation of the constructed models for further analysis/synthesis by external tools. Flexibility and extensibility of ForSyDe is emphasized by integrating a new timed model of computation without central synchronization, and by providing mechanisms for integrating foreign models, parallel and distributed simulation, modeling adaptive, data-parallel, and non-deterministic systems. A set of ForSyDe features is demonstrated in practice, and compared with similar approaches using a running example and two relevant case studies.