Path-Directed Abstraction and Refinement for SAT-Based Design Debugging
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
Functional verification has become one of the most time-consuming tasks in the very large scale integration design flow accounting for up to 57% of the total project time. The largest component of this task is that of design debugging due to its resource-intensive manual nature. With the ever growing size of modern designs and their error traces, the complexity of the debugging problem poses a great challenge to automated debugging techniques. To overcome this challenge, this paper introduces a novel path-directed abstraction and refinement algorithm for design debugging to manage excessive error trace lengths. A sliding window of the error trace is iteratively analyzed in a time-windowing framework, which is made possible by the use of the path-directed abstraction. This abstraction forms a concise approximation of nonmodeled parts of the error trace while simultaneously providing an efficient representation for refinement. The result is an algorithm that dramatically reduces the memory requirements of debugging while mitigating the incomplete results of past techniques. Experimental results on industrial designs with long error traces show that the proposed approach can analyze traces that are 64.6% longer while simultaneously decreasing peak memory usage compared to previous work.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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