Safer PI ISIS May 00 Seattle

Information about Safer PI ISIS May 00 Seattle

Published on September 25, 2007

Author: Nickel

Source: authorstream.com

Content

Slide1:  Model-Integrated Approach to Adaptive Embedded Systems Akos Ledeczi Institute for Software Integrated Systems Vanderbilt University Outline:  Outline Key benefits of MIC Static (design-time) adaptivity Dynamic (run-time) adaptivity Conclusions Benefits of MIC:  Benefits of MIC Level of abstraction: source code vs. high-level system models Constraints: implicit vs. explicit constraints Scope: point solution vs. design space System Models:  System Models Domain-specific language Primarily graphical Multiple-aspects Hierarchical decomposition Model: representation of all the information that is necessary to automatically synthesize the application Constraints:  Constraints Domain-specific: model-integrity constraints Application-specific: compositional resource performance cost etc. Scope: Design-Space:  Scope: Design-Space Explicit alternatives andgt;andgt;andgt; finite design space Parametric, generative models: architectural parameters algorithmic description andgt;andgt;andgt; infinite design space Design-Space Exploration:  Design-Space Exploration Step 1: Search-space pruning symbolic constraint satisfaction (e.g. OBDDs) interactive and iterative (selective constraint relaxation) input: 10n (n andgt;andgt; 1); output: 1-10 configurations Step 2: Simulation Step 3: Optimization Step 4: Automatic system synthesis Automatic System Synthesis:  Automatic System Synthesis Hardware configuration (e.g. VHDL) Source code (C, C++, Java, SQL etc.) Configuration information: real-time schedules message routing maps assignment etc. Slide9:  Target Application - Formal constraints MIC Approach System Models System Synthesis Slide10:  S1 S3 S2 e1[S21]/ / /../ /../ (D1.time - D2.time) andlt; 2 P3 (mode=(S1 or S2))implies(P1=P1i) Pr1 Pr3 C1 if(k = 2 and n andlt; 4) generate(n) else generate(k) Functional models Resource models Behavior models Explicit alternatives Performance constraint Compositional constraint Hierarchical Parallel FSM G n k Generator Parameters Explicit alternatives Design Space Modeling Slide11:  Design Space Pruning Slide12:  Modeling objects are lost at generation step: Fixed configuration or mode-based run-time adaptivity only No incremental changes No feedback to model Static (Design-Time) Adaptivity Models Synthesis Config / source files Application Slide13:  Embedded synthesizer Dynamic (Run-Time) Adaptivity Models Synthesis Application Embedded models replace (augment) configuration / source files: self-describing system incremental change capability system state is communicated in terms of model-based information Adaptive Behavior:  Adaptive Behavior Monitoring Analysis, redesign, verification Reconfiguration Monitoring:  Monitor variables reflect system state in the model What to monitor? Kernel/OS status Application state Events, exceptions Monitoring Embedded synthesizer Analysis, Redesign, Verification:  The evaluator is a system process Varying complexity: table-driven model-defined logic procedural etc. Generator scripts: Controlled by their input parameters in the model Cannot legally be bypassed to change model Reliably handle transitions, sequencing and incremental changes Constraint manager Analysis, Redesign, Verification Embedded synthesizer R/T Executive process 2 process 3 process 4 Evaluator Constraint Manager Generator scripts Evaluator Reconfiguration:  Reconfiguration Embedded synthesizer The embedded synthesizer is a system process Relatively simple translation Conclusions:  Conclusions High-level system models and explicit constraints: postpone implementation decisions as late as possible Design space modeling and automatic system synthesis: amortize initial investment over the lifetime of the application Embedded models and synthesis: self-describing system

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