lect2 - Lecture 2 Lecture Computation Models and...

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Lecture 2 Lecture 2 Computation Models and Abstractions: Computation Models and Abstractions: Properties of Abstract Models Time– Real, Relative, and Constrained Simplest Embedded Systems Forrest Brewer
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Models and Abstractions Models and Abstractions Foundations of science and engineering Activities usually start with informal specification Writing on back of a napkin, project reports Models and Abstractions soon follow Abstraction enables decomposition of systems into (simpler) sub-systems Chess – components (pieces), composition rules (playing board, movement rules) Models provide structure on which analysis and optimization are possible Behavior is externally visible events based on internal interactions of abstract components Properties are constraints met by all Behaviors of a system Sometimes properties can be induced from structure (composition) Models from classical CS FSM (finite-sate machine), RAM (random access memory) (von Neumann) CSP (Communicating Sequential Processes) (Hoare), CCS (Calculus of Communicating Systems) (Milner) Pushdown automata, Turing machine
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Methodical System Design Methodical System Design Ad-hoc design does not work beyond a certain level of complexity that is exceeded by large number of embedded systems Methodical, engineering-oriented, tool-based approach is essential specification, synthesis, optimization, verification etc. prevalent for hardware, still rare for software One key aspect is the creation of models concrete representation of knowledge and ideas about a system being developed - specification model deliberately modifies or omits details (abstraction) but concretely represents certain properties to be analyzed, understood and verified one of the few tools for dealing with complexity
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Good Models Good Models Simple Amenable for development of theory Theorems allow for generalizations and short-cuts Should not be too general (theorems become too weak) High Expressive Power Compact representation enables higher productivity Provides Ability for Critical Reasoning Executable Simulation/Validation Synthesizable Usually requires design orthogonality (e.g. compiler) Unbiased towards any specific implementation Extremely hard to achieve, but worth it. Fit the task at hand If the model doesn’t fit, too much work is needed to use it…
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Common Models of Systems Common Models of Systems Finite State Machines/Regular Languages finite state full concurrency (notion of state) Data-Flow/Process Models Partial Order Concurrent and sometimes Determinate Stream of computation Discrete-Event Global Order (embedded in time) Resolution Limits Distributed-Event Locally Discrete, Globally Asynchronous Network Models Continuous Time Simulation (Discrete
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This note was uploaded on 12/29/2011 for the course ECE 253 taught by Professor Brewer,f during the Fall '08 term at UCSB.

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lect2 - Lecture 2 Lecture Computation Models and...

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