Bibliographic record
Abstract
Transaction-level modeling allows exploring several SoC design architectures, leading to better performance and easier verification of the final product. In this paper, we present an approach to design and verify SystemC models at the transaction level. We integrate the verification as part of the design flow where we first model both the design and the properties (written in Property Specification language) in Unifed Modeling Language (UML); then, we translate them into an intermediate format modeled with AsmL [language based on Abstract State Machines (ASM)]. The AsmL model is used to generate a finite state machine of the design, including the properties. Checking the correctness of the properties is performed on the fly while generating the state machine. Finally, we translate the verified design to SystemC and map the properties to a set of assertions (as monitors in C#) that can be reused to validate the design at lower levels by simulation. For existing SystemC designs, we propose to translate the code back to AsmL in order to apply the same verification approach. At the SystemC level, we also present a genetic algorithm to enhance the assertions coverage. We will ensure the soundness of our approach by proving the correctness of the SystemC-to-AsmL and AsmL-to-SystemC transformations. We illustrate our approach on two case studies including the PCI bus standard and a master/slave generic architecture from the SystemC library.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".