Tutorial T10: Post - Silicon Validation, Debug and Diagnosis
Bibliographic record
Abstract
Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Drastic increase in design complexity along with the emergence of new failure mechanisms in the nanometer regime has led to significant increase in the complexity of verification, validation, and debug of integrated circuits. In spite of extensive efforts, it is not always possible to detect all the functional errors and electrical faults during pre-silicon validation. Post-silicon validation is used to detect design flaws including the escaped functional errors as well as electrical faults. In this tutorial, we will provide a comprehensive coverage of both fundamental concepts and recent advances in post-silicon validation, debug and diagnosis. The tutorial presenters (3 industry experts and 3 faculty members) will provide unique perspectives on both academic research and industrial practices. First, we will discuss various challenges associated with post-silicon validation and debug. Next, we will describe various techniques for automated generation of directed tests to activate both functional errors and electrical faults. We will cover recent advances in observability enhancement through signal selection and low-overhead trace hardware design. We will also describe various state-of-the-art post-silicon debug approaches for modern microprocessors and SoC designs. Next, we will present examples of real-life design failures, and successful debug scenarios in industrial settings. Finally, we will conclude the tutorial with discussion on emerging issues and future directions for successful postsilicon validation and debug.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.062 | 0.043 |
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 source (direct Gemma or distilled Codex), 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".