JASDSE Framework
A Joint Analytical and Simulation (JAS) based Design Space Exploration (DSE) framework, i.e. a JASDSE framework, is a DSE framework capable to combine the benefits of analytical and simulation based methods in DSE. Analytical methods enable fast assessment and exploration. Simulationbased methods enable accuracy and better possibility to optimize the system for given working environment.
KTH/ES is building a JASDSE framework which has been initially posed in the Forum for Specification and Design Languages,( FDL'13). There it is explained how this framework can serve for an efficient design of more efficient time critical systems and mixed criticality systems. The framework is sketched in the following figure:
The JASDSE framework integrates DSE based on worstcase analytical models (WCADSE) with scenarioaware and simulationbased (SAS) DSE. For the WCADSE, the framework relies on constraint programming.
Proof of Concept
In FDL'13, a first proof of concept demonstrator (whose sources will be available soon) was presented. For it, a proofofconcept flow was implemented and sketched following:
Such proofofconcept flow supported the capture system model as the one sketched as follows:
A constraint on the maximum response time was imposed in a former analytical phase. That enabled to get all compliant solutions. Moreover, the Pareto set after applying only the analytical phase (in red in the following picture) was compared to the Pareto set if the same set of solutions compliant with the critical constraint (safe set or compliant set) is assessed by means of simulation to obtain a more accurate Pareto set according the environment scenario (in blue).
What this figure, and thus this proofofconcept illustrated is that the proposed JASDSE can reveal a different Pareto set, regarding the one derived from the application of the analytical DSE. Therefore, the JASDSE can help to find more efficient solutions (in resources, power consumption, etc) without sacrificing any time guarantee already provided by the analytical phase.
Why is this improvement possible?
The following figure illustrates a main reason why a simulationbased phase can provide better accuracy.
It shows an histogram of the frequency of the response time values of one of the functions in the proof of concept example. In red, the histogram resulting from the application of 10 million random input vectors is shown. The figures overlaps an histogram which represents in green the response times of the functionality which are actually given when the whole system is stimulated by a given environment scenario. It shows that an analytical approach based on worstcase times falls in such a case in an overpessimis. Moreover, since the worstcase workloads are considered to happen at the same time for all actors of the application, the analtical model is likely considering false paths, that is, execution paths (and thus response paths) which cannot be given for any feasible input to the system.
Moreover, the FDL'13 example did not consider the impact of communication performance modelling. However, depending on the case, it can be also very relevant, and the consideration of worstcase communication times as safe bounds in analytical models, is an additional source of overpessimism and false paths which make the SASDSE phase worth for the search of more efficient solutions.
Current Research
KTH/ES is generalizing and growing this framework, based on several tools associated to the ForSyDe modelling and design framework and tools from third party partners.
At the frontend, the connection with the ForSyDeSystemC methodology is in progress. The connection with a modeldriven frontend is also of interest.
For the WCADSE phase, the framework is relying on analytical DSE tool. This is enabling a more generic application model (acyclic and cyclic SDF graphs, multiple applications, periodic task sets), and the consideraton of a more generic predictable platform, considering a TDMA bus model. This will also enable to get the user rid of the details of the syntax of constraintprogramming (which is handled in the FDL'13 proofofconcept.
For the SASDSE, the framework relies on the performance estimation tool KisTA tool and the exploration tool MOST, developped by the Politecnico di Milano, which has its open counterpart in the MExplorer tool. The integration of these tools will enable a faster JASDSE framework.
Many other enhancements are foreseen. An advance of the most important is given in the book of "The best from FDL'13" to be published soon.
Attachments

combined_dse.png
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added by fernanhc 3 years ago.
Sketch of the generic JASDSE flow proposed

system_example.png
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added by fernanhc 3 years ago.
sketch of the system example

times_f3_sc1.png
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added by fernanhc 3 years ago.
histogram_f2

combined_dse_instance.png
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added by fernanhc 3 years ago.
Sketch of the combined DSE instance for the FDL13 paper

pareto1_new_interesting_sol.png
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added by fernanhc 3 years ago.
Graph showing how SASSDE phase can reveal a different Pareto set and thus more efficient solutions