Advances in Debug Automation for a Modern Verification Environment
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
Over the past three decades, the growing list of requirements for integrated circuits has continually presented new challenges to the electronic design community. One of the biggest challenges in the design process is that of functional debugging, which aims to find the root-cause of a functional failure after it has been detected. In recent years, this key challenge has grown in size and scope as bugs commonly appear in both the design and verification environment. This increase in size and scope has made functional debugging one of the largest bottlenecks in the design cycle and points to an urgent need for more scalable and innovative debugging solutions. \n \nThis dissertation presents multiple novel contributions that address the challenges of increased size and scope of modern functional debugging. In particular, these contributions address the scalability of existing automated design debugging techniques, as well as introduce novel automated tools specifically for debugging the verification environment. \n \nThe first contribution introduces an unsatsifiable core-guided abstraction and refinement technique for design debugging that focuses on managing the design size aspect of debugging complexity. The second contribution introduces a path-directed abstraction and refinement technique that aims to manage the error trace length aspect of debugging complexity. The third contribution presents a novel method that utilizes unsatisfiable cores in design debugging to manage the multiple design errors aspect of debugging complexity. The fourth contribution presents an automated technique to aid debugging of errors found within formal properties themselves. The final contribution presents an automated technique to aid debugging of missing assumptions that are needed during verification methodologies that use formal methods.
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.
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 | 0.001 |
| Open science | 0.001 | 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