Embedded system design challenges

Embedded system design challenges

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  EMBEDDED SYSTEMS DESIGN CHALLENGES Henzinger,ThomasA., and Joseph Sifakis. "The embedded systems design challenge." International Symposium on Formal Methods. Springer Berlin Heidelberg, 2006. Aditya Kamble
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  Abstract Embedded systems design Currentscientific foundations Hardware v/s software Analytical v/s computational Current engineering practices Model-based Critical v/s Best-effort engineering Heterogeneity Constructivity
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  Embedded Systems Design •Systems design is the process of deriving, from requirements, a model or an abstract representation of a system from which a system can be generated more or less automatically. • In embedded systems, holistic approach that integrates essential paradigms from hardware design, software design, and control theory in a consistent manner.
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  CURRENT SCIENTIFIC FOUNDATIONS
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  Hardware versus softwaredesign Hardware • Parallel components • Represented by analytical models (equations) • Models specify their transfer functions. • How data flows across multiple components. • Deterministic (or probabilistic) models • Correct functionality, and even performance and robustness are often specified discretely Software • Sequential components • Represented by computational models (programs) • Semantics of models is defined operationally by an abstract execution engine • How control flows across multiple components. • Models may be nondeterministic • Continuous performance and robustness measures refer to physical resource levels
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  Analytical versus Computational Modeling Analytical •Electrical Engineering • Equation based • Eg. Netlist representation of a circuit • Deal naturally with concurrency and with quantitative constraints • Difficulties with partial and incremental specifications and with computational complexity. Computational • Computer Science • Program based • Eg. Program written in an imperative language • Naturally support nondeterministic abstraction hierarchies and a rich theory of computational complexity, • Difficulties dealing with concurrency and incorporating physical constraints.
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  CURRENT ENGINEERING PRACTICES
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  Model-based Design Language-based methods •Lie in the software tradition • Centered on a particular programming language with a particular target run-time system • Eg. Ada, RT-Java Synthesis-based methods • Come out of the hardware tradition • Start from a system description in a tractable and structural fragment of a hardware description language derive an implementation satisfying constraints. • Eg.VHDL andVerilog
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  Model-based technologies • Thesynchronous languages embody an abstract hardware semantics (synchronicity) within different kinds of software structures (functional; imperative). • Eg. Lustre and Esterel • Implementations require a separation between the components to be implemented in hardware, and those to be implemented in software; different design-space exploration techniques provide guidance in making such partitioning decisions. • Modern technologies attempt to be more generic in their choice of semantics and bring extensions in two directions: independence from a particular programming language; and emphasis on system architecture as a means to organize computation, communication, and constraints. • UML and AADL
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  Weakness • “The lackof analytical tools for computational models to deal with physical constraints; and the difficulty to automatically transform noncomputational models into efficient computational ones” • Need to better understand relationships and transformations between heterogeneous models.
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  Model transformations • Designprocess by iterating model construction, model analysis, and model transformation. • Difficult to manually improve on the code produced by a good optimizing compiler from programs(computational models). • Code generators often produce inefficient code from equation-based models. • Develop high-level programming languages that can express reaction constraints, together with compilers that guarantee the preservation of the reaction constraints on a given execution platform.
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  Critical versus Best-EffortEngineering Critical Engineering • Tries to guarantee system safety at all costs • Design as a constraint-satisfaction problem • Based on worst-case analysis and on static resource reservation • Eg. safety-critical systems in avionics • Satisfy dependability, ‘hard’ worst- case requirements Best-Effort Engineering • Tries to optimize system performance (and cost) • Design as an optimization problem • based on average-case analysis and on dynamic resource allocation • Eg. telecommunications. • Different degrees of satisfaction, soft deadlines (QoS)
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  Bridging the gap •The increasing computing power of system-on-chip and network-on-chip technologies allows the integration of critical and noncritical applications on a single chip. • Reduction in communication costs and increase in hardware reliability. • More rational and cost-effective management of resources. • Treat the satisfaction of critical requirements as minimal guaranteed QoS level. • Integration of boolean-valued and quantitative methods.
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  Two Demands ona Solution • Heterogeneity: property of embedded systems to be built from components with different characteristics. • Constructivity: possibility to build complex systems that meet given requirements, from building blocks and glue components with known properties.
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  Encompassing Heterogeneity • Themeaningful composition of heterogeneous components to ensure their correct interoperation. • The meaningful refinement and integration of heterogeneous viewpoints during the design process. • Execution semantics. • Synchronous execution: system’s execution as a sequence of global steps. • Asynchronous execution: does not use any notion of global computation step • Interaction semantics: combinations of actions performed by system components in order to achieve a desired global behaviour. • Atomic • Non-atomic
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  Achieving Constructivity • “Builda system meeting a given set of requirements from a given set of components” • Hardware synthesis techniques, software compilation techniques, algorithms (e.g., for scheduling, mutual exclusion, clock synchronization), architectures (such as time-triggered; publish-subscribe), as well as protocols (e.g., for multimedia synchronization) are few solutions. • Compositionality rules: infer global system properties from the local properties of subsystems • Non-interference rules: guarantee that during the system construction process, all essential properties of subsystems are preserved
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  Summary • We needa mathematical basis for systems modeling and analysis which integrates both abstract-machine models and transfer-function models in order to deal with computation and physical constraints in a consistent and operative manner. • It should be possible to combine practices for critical systems engineering to guarantee functional requirements, with best-effort systems engineering to optimize performance and robustness. • The theory, the methodologies, and the tools need to encompass heterogeneous execution and interaction mechanisms for the components of a system, and they need to provide abstractions that isolate the sub problems in design that require human creativity from those that can be automated.
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  THANKYOU!