A Model-Driven Design Framework for Massively Parallel Embedded Systems
Why this work is in the frame
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Bibliographic record
Abstract
Modern embedded systems integrate more and more complex functionalities. At the same time, the semiconductor technology advances enable to increase the amount of hardware resources on a chip for the execution. Massively parallel embedded systems specifically deal with the optimized usage of such hardware resources to efficiently execute their functionalities. The design of these systems mainly relies on the following challenging issues: first, how to deal with the parallelism in order to increase the performance; second, how to abstract their implementation details in order to manage their complexity; third, how to refine these abstract representations in order to produce efficient implementations. This article presents the Gaspard design framework for massively parallel embedded systems as a solution to the preceding issues. Gaspard uses the repetitive Model of Computation (MoC), which offers a powerful expression of the regular parallelism available in both system functionality and architecture. Embedded systems are designed at a high abstraction level with the MARTE (Modeling and Analysis of Real-time and Embedded systems) standard profile, in which our repetitive MoC is described by the so-called Repetitive Structure Modeling (RSM) package. Based on the Model-Driven Engineering (MDE) paradigm, MARTE models are refined towards lower abstraction levels, which make possible the design space exploration. By combining all these capabilities, Gaspard allows the designers to automatically generate code for formal verification, simulation and hardware synthesis from high-level specifications of high-performance embedded systems. Its effectiveness is demonstrated with the design of an embedded system for a multimedia application.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it