Bibliographic record
Abstract
Transaction Level Models (TLMs) are being increasingly used by multi-core system designers for design validation and embedded SW development. However, with well defined modeling semantics and TLM automation tools, it is also possible to use TLMs for multi-core design. This paper presents recent research in automatic generation of timed TLMs for early, yet reliable, evaluation of multi-core design decisions. The TLMs are automatically generated from a given mapping of a concurrent application to a multi-core platform. The application code is annotated with delays at the basic-block level of granularity. Similarly, the platform services, such as communication and scheduling, also include timing delays. The TLM automation methods have been implemented in the Embedded System Environment (ESE) toolset. Our experimental results with ESE demonstrate that multi-core TLMs can be generated in the order of seconds; they simulate close to host-compiled application execution speed, and are more than 90% accurate compared to board measurements on average for industrial size examples. Therefore, TLM automation enables early and reliable evaluation of multi-core design decisions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".