Bibliographic record
Abstract
Recent advances in hardware design has enabled integration of a complete yet complex systems on a single chip (called System-on-a-Chip: SoC). It is conceivable that the role of traditional Register Transfer level (RTL) languages will diminish to an extent akin to assembly level languages in software design. Therefore, new design languages or so-called System Level Languages (SLL) have emerged. Verification techniques for SOC designs also need to change with this trend. Combining classical verification techniques, such as simulation, with several other formal techniques, into a single approach has been gaining attention in SoC verification. Classical simulation based verification techniques when used with SystemC face several problems related to the object-oriented aspect of SystemClibrary and due to the complexity of its simulation environment. In this talk, we present our proposed methodology to verify SoC designs modeled in SystemC. To this end, we introduce a hybrid approach combining static code analysis, model checking and assertion based verification. We also propose to augment the approach by a test generation module in order to improve the coverage metrics in comparison to the classical simulation approach (mainly based on random test generation)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".