A succinct memory model for automated design debugging
Bibliographic record
Abstract
In todaypsilas complex SoC designs, verification and debugging are becoming ever more crucial and increasingly time-consuming tasks. The prevalence of embedded memories adds to the difficulty of the problem by exponentially increasing the state-space of the design. In this work, a novel memory model for design debugging is presented. It models memory succinctly by avoiding an explicit representation for each memory bit. The method uses the simulation of the erroneous design to guide the debugging process. This results in a parameterizable formal encoding that grows linearly with the erroneous trace length, significantly reducing the memory requirements of the debugging problem. In addition, the proposed model is extended to handle an arbitrary initial memory configuration, as well as non-cycle accurate output traces where only a final expected memory state is available for comparison. Experiments on industrial designs show a 96% average reduction in memory usage along with a noticeable performance improvement compared to previous work.
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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".