Accurate timed RTOS model for transaction level modeling
Bibliographic record
Abstract
In this paper, we present an accurate timed RTOS model within transaction level models (TLMs). Our RTOS model, implemented on top of system level design language (SLDL), incorporates two key features: RTOS behavior model and RTOS overhead model. The RTOS behavior model provides dynamic scheduling, inter-process communication (IPC), and external communication for timing annotated user applications. While the RTOS behavior model is running, all RTOS events, such as context switch and interrupt handling, are passed to RTOS over-head model to adopt the overhead during system execution. Our RTOS overhead model has processor- and RTOS-specific pre-characterized overhead information to provide cycle approximate estimation. We demonstrate the applicability of our model using a multi-core platform executing a JPEG encoder. Experimental results show that the proposed RTOS model provides the high accuracy, 7% off compared to on-board measurements while simulating at speeds close to the reference C code.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".