402 research outputs found

    Distributed real-time operating system (DRTOS) modeling in SpecC

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    System level design of an embedded computing system involves a multi-step process to refine the system from an abstract specification to an actual implementation by defining and modeling the system at various levels of abstraction. System level design supports evaluating and optimizing the system early in design exploration.;Embedded computing systems may consist of multiple processing elements, memories, I/O devices, sensors, and actors. The selection of processing elements includes instruction-set processors and custom hardware units, such as application specific integrated circuit (ASIC) and field programmable gate array (FPGA). Real-time operating systems (RTOS) have been used in embedded systems as an industry standard for years and can offer embedded systems the characteristics such as concurrency and time constraints. Some of the existing system level design languages, such as SpecC, provide the capability to model an embedded system including an RTOS for a single processor. However, there is a need to develop a distributed RTOS modeling mechanism as part of the system level design methodology due to the increasing number of processing elements in systems and to embedded platforms having multiple processors. A distributed RTOS (DRTOS) provides services such as multiprocessor tasks scheduling, interprocess communication, synchronization, and distributed mutual exclusion, etc.;In this thesis, we develop a DRTOS model as the extension of the existing SpecC single RTOS model to provide basic functionalities of a DRTOS implementation, and present the refinement methodology for using our DRTOS model during system level synthesis. The DRTOS model and refinement process are demonstrated in the SpecC SCE environment. The capabilities and limitations of the DRTOS modeling approach are presented

    Electronic System-Level Synthesis Methodologies

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    Efficient Refinement Checking in VCC

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    We propose a methodology for carrying out refinement proofs across declarative abstract models and concrete implementations in C, using the VCC verification tool. The main idea is to first perform a systematic translation from the top-level abstract model to a ghost implementation in VCC. Subsequent refinement proofs between successively refined abstract models and between abstract and concrete implementations are carried out in VCC. We propose an efficient technique to carry out these refinement checks in VCC. We illustrate our methodology with a case study in which we verify a simplified C implementation of an RTOS scheduler, with respect to its abstract Z specification. Overall, our methodology leads to efficient and automatic refinement proofs for complex systems that would typically be beyond the capability of tools such as Z/Eves or Rodin

    Intelligent Embedded Software: New Perspectives and Challenges

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    Intelligent embedded systems (IES) represent a novel and promising generation of embedded systems (ES). IES have the capacity of reasoning about their external environments and adapt their behavior accordingly. Such systems are situated in the intersection of two different branches that are the embedded computing and the intelligent computing. On the other hand, intelligent embedded software (IESo) is becoming a large part of the engineering cost of intelligent embedded systems. IESo can include some artificial intelligence (AI)-based systems such as expert systems, neural networks and other sophisticated artificial intelligence (AI) models to guarantee some important characteristics such as self-learning, self-optimizing and self-repairing. Despite the widespread of such systems, some design challenging issues are arising. Designing a resource-constrained software and at the same time intelligent is not a trivial task especially in a real-time context. To deal with this dilemma, embedded system researchers have profited from the progress in semiconductor technology to develop specific hardware to support well AI models and render the integration of AI with the embedded world a reality
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