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Model-integrated development of embedded software
- Proceedings of the IEEE
, 2003
"... Proceedings of the IEEE January 2003 The paper describes a model-integrated approach for embedded software development that is based on domain-specific, multiple view models used in all phases of the development process. Models explicitly represent the embedded software and the environment it operat ..."
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Cited by 163 (33 self)
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Proceedings of the IEEE January 2003 The paper describes a model-integrated approach for embedded software development that is based on domain-specific, multiple view models used in all phases of the development process. Models explicitly represent the embedded software and the environment it operates in, and capture the requirements and the design of the application, simultaneously. Models are descriptive, in the sense that they allow the formal analysis, verification and validation of the embedded system at design time. Models are also generative, in the sense that they carry enough information for automatically generating embedded systems using the techniques of program generators. Because of the widely varying nature of embedded systems, a single modeling language may not be suitable for all domains, thus modeling languages are often domain-specific. To decrease the cost of defining and integrating domain-specific modeling languages and corresponding analysis and synthesis tools, the model-integrated approach is applied in a metamodeling architecture, where formal models of domain-specific modeling languages – called metamodels – play a key role in customizing and connecting components of tool chains. The paper will discuss the principles and techniques of model-integrated embedded software development in detail, as well as the capabilities of the tools supporting the process. Examples in terms of real systems will be given that illustrate how the model-integrated approach addresses the physical nature, the assurance issues, and the dynamic structure of embedded software.
Parameterized Dataflow Modeling for DSP Systems
- IEEE TRANSACTIONS ON SIGNAL PROCESSING
, 2001
"... Dataflow has proven to be an attractive computation model for programming digital signal processing (DSP) applications. A restricted version of dataflow, termed synchronous dataflow (SDF), that offers strong compile-time predictability properties, but has limited expressive power, has been studied e ..."
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Cited by 117 (41 self)
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Dataflow has proven to be an attractive computation model for programming digital signal processing (DSP) applications. A restricted version of dataflow, termed synchronous dataflow (SDF), that offers strong compile-time predictability properties, but has limited expressive power, has been studied extensively in the DSP context. Many extensions to synchronous dataflow have been proposed to increase its expressivity while maintaining its compile-time predictability properties as much as possible. We propose a parameterized dataflow framework that can be applied as a meta-modeling technique to significantly improve the expressive power of any dataflow model that possesses a well-defined concept of a graph iteration. Indeed, the parameterized dataflow framework is compatible with many of the existing dataflow models for DSP including SDF, cyclo-static dataflow, scalable synchronous dataflow, and Boolean dataflow.In this paper, we develop precise, formal semantics for parameterized synchr...
What's Ahead for Embedded Software?
- Software?”, Computer
, 2000
"... at "components" and "frameworks" might entail. Otherwise, we have little hope of getting a useful model because the prevailing component architectures in software engineering are not suitable for embedded systems. Most frameworks have four service categories: . Ontology. A f ..."
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Cited by 106 (11 self)
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at "components" and "frameworks" might entail. Otherwise, we have little hope of getting a useful model because the prevailing component architectures in software engineering are not suitable for embedded systems. Most frameworks have four service categories: . Ontology. A framework defines what it means to be a component. Is a component a subroutine? A state transformation? A process? An object? An aggregate of components may or may not be a component. Certain semantic properties of components also flow from the definition. Is a component active or passive---can it autonomously initiate interactions with other components or does it simply react to stimulus? . Epistemology. A framework defines states of knowledge. What does the framework know about the components? What do components know about one another? Can components interrogate one another to obtain information (that is, is there reflection or introspection)? What do components know<F1
Instruction Generation for Hybrid Reconfigurable Systems
- ACM Transactions on Design Automation of Electronic Systems
, 2001
"... Building Blocks (ABBs), or instructions available from a given hardware library. The customized data path generated from many ABBs was referred to as an application specific unit (ASU). Cathedral's synthesis targeted ASUs, which could be executed in very few clock cycles. This goal was achieved ..."
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Cited by 74 (6 self)
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Building Blocks (ABBs), or instructions available from a given hardware library. The customized data path generated from many ABBs was referred to as an application specific unit (ASU). Cathedral's synthesis targeted ASUs, which could be executed in very few clock cycles. This goal was achieved via manual clustering of necessary operations into more compact operations, essentially a form of template construction. Whereas our template generation and matching algorithms are automated, the definition of clusters in Cathedral was a manual operation, mainly clustering loop and function bodies. Their results demonstrated an expected reduction of critical path length as well as interconnect as a result of clustering.
Heterogeneous Concurrent Modeling and Design in Java (Volumes 1: Introduction to Ptolemy II)
, 2005
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Embedded Software
- Advances in Computers
, 2002
"... The science of computation has systematically abstracted away the physical world. Embedded software systems, however, engage the physical world. Time, concurrency, liveness, robustness, continuums, reactivity, and resource management must be remarried to computation. Prevailing abstractions of compu ..."
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Cited by 63 (7 self)
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The science of computation has systematically abstracted away the physical world. Embedded software systems, however, engage the physical world. Time, concurrency, liveness, robustness, continuums, reactivity, and resource management must be remarried to computation. Prevailing abstractions of computational systems leave out these "non-functional" aspects. This chapter explains why embedded software is not just software on small computers, and why it therefore needs fundamentally new views of computation. It suggests component architectures based on a principle called "actor-oriented design," where actors interact according to a model of computation, and describes some models of computation that are suitable for embedded software. It then suggests that actors can define interfaces that declare dynamic aspects that are essential to embedded software, such as temporal properties. These interfaces can be structured in a "system-level type system" that supports the sort of design-time and run-time type checking that conventional software benefits from.
Semantic anchoring with model transformations
- In ECMDA-FA, volume 3748 of LNCS
, 2005
"... Abstract. Model-Integrated Computing (MIC) is an approach to Model-Driven Architecture (MDA), which has been developed primarily for embedded systems. MIC places strong emphasis on the use of domain-specific modeling languages (DSML-s) and model transformations. A metamodeling process facilitated by ..."
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Cited by 46 (7 self)
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Abstract. Model-Integrated Computing (MIC) is an approach to Model-Driven Architecture (MDA), which has been developed primarily for embedded systems. MIC places strong emphasis on the use of domain-specific modeling languages (DSML-s) and model transformations. A metamodeling process facilitated by the Generic Modeling Environment (GME) tool suite enables the rapid and inexpensive development of DSML-s. However, the specification of semantics for DSML-s is still a hard problem. In order to simplify the DSML semantics, this paper discusses semantic anchoring, which is based on the transformational specification of semantics. Using a mathematical model, Abstract State Machine (ASM), as a common semantic framework, we have developed formal operational semantics for a set of basic models of computations, called semantic units. Semantic anchoring of DSML-s means the specification of model transformations between DSML-s (or aspects of complex DSML-s) and selected semantic units. The paper describes the semantic anchoring process using the meta-programmable MIC tool suite. 1
An Extensible Type System for Component-Based Design
, 2002
"... Abstract. We present the design and implementation of the type system for Ptolemy II, which is a tool for component-based heterogeneous modeling and design. This type system combines static typing with run-time type checking. It supports polymorphic typing of components, and allows automatic lossles ..."
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Cited by 41 (10 self)
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Abstract. We present the design and implementation of the type system for Ptolemy II, which is a tool for component-based heterogeneous modeling and design. This type system combines static typing with run-time type checking. It supports polymorphic typing of components, and allows automatic lossless type conversion at run-time. To achieve this, we use a lattice to model the lossless type conversion relation among types, and use inequalities defined over the type lattice to specify type constraints in components and across components. The system of inequalities can be solved efficiently, with existence and uniqueness of a solution guaranteed by fixed-point theorems. This type system increases the safety and flexibility of the design environment, promotes component reuse, and helps simplify component development and optimization. The infrastructure we have built is generic in that it is not bound to one particular type lattice. The type system can be extended in two ways: by adding more types to the lattice, or by using different lattices to model different system properties. Higher-order function types and extended types can be accommodated in this way. 1
