White-box verification techniques in Networking ASIC Design”, Thesis Dissertation
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
The following document describes a study of new concepts in ASIC verification. It was performed at Nortel Networks in Ottawa, Canada. The work was done as part of an ASIC design project. This report outlines the general ideas and conclusions, the technical and proprietary details have been presented separately at Nortel. I would like to thank Gregory King at Nortel Networks for his guidance and support during my time at Nortel. His experience and advice was invaluable to the completion of this thesis. I also wish to thank Roger Sabbagh at Zero-In Design Automation for guiding me through the application and methodologies of a whitebox verification tool. Also a thanks to everyone else that I worked with at Nortel for sharing their experience and knowledge, and finally Jan-Eric Larsson, my supervisor at Lund University for giving valuable feedback during the completion of this report.
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 | 0.000 |
| 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