A Novel Modeling Approach for System-Level Application Mapping Targeted for Configurable Architecture
Bibliographic record
Abstract
Advances in chip fabrication technology and increasing demand for meeting time to market have led to the use of the electronic system-level (ESL) design methodology. An important challenge is to find a proper approach by which a given application can be mapped into a specific target architecture, usually called application mapping. In this paper, an abstract modeling approach based on the colored Petri net (CPN) is proposed to map an arbitrary application into a given target architecture. The mapping is at an abstract level and contains timing information that facilitates high-level design space exploration. A complete stepwise procedure is presented to illustrate how a CPN model of the application is refined and employed to extract the required tasks for a given target architecture. The fact that models obtained as such are executable makes the required performance evaluation and exploration possible, and thus, will result in better architectural decisions at the design stage. The usefulness of the proposed approach is assessed using several alternative mapping schemes of the JPEG encoder. The actual encoding time and the required simulation time indicate the advantages of this method over design-space exploration that would otherwise be done in SystemC, which is the natural choice in today's ESL designs.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".