On-Chip Cube-Based Constrained-Random Stimuli Generation for Post-Silicon Validation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Post-silicon validation is critical for exposing subtle design errors that have escaped to the silicon prototypes. Its effectiveness is conditioned by in-system application of a large volume of functionally-compliant stimuli. In this paper, we present a methodology to design constrained-random stimuli generators, which are placed on-chip and are configurable at design-time to generate in-system functionally-compliant stimuli subject to user-programmable constraints provided at validation time. Central to our method is a cube-based representation of constraints. These cubes are used as masks that force pseudo-random sequences to map onto functionally-compliant stimuli. To reduce the on-chip storage requirements, masks are compressed at design-time and expanded on-the-fly at validation time using decompression circuitry. Experimental results evaluate the impact of our method on the requirements for on-chip logic and memory resources.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it