Generating Cyclic-Random Sequences in a Constrained Space for In-System Validation
Bibliographic record
Abstract
The constrained-random methodology is widely used during the pre-silicon verification of very-large scale integrated circuits. Recently, research efforts have been made to support the application of constrained-random patterns during the post-silicon validation stage. In this paper, we present a new method, including both software algorithms and on-chip hardware structures, for in-system constrained-random generation of stimuli sequences that are uniformly distributed. More specifically, we facilitate in-system application of constrained-random sequences that are cyclic-random, i.e., all the valid values from the user-constrained space are generated only once before the entire sample space is exhausted. While software simulation environments commonly support this feature, e.g., randc in SystemVerilog, to the best of our knowledge this is the first time it is shown how such feature can be ported to hardware environments.
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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".