Hierarchical Embedded Logic Analyzer for Accurate Root-Cause Analysis
Bibliographic record
Abstract
Post-silicon debugging process is aimed at locating errors not detected during the process of pre-silicon verification. Although in the post-silicon validation engineers can exploit the high speed of hardware prototype to exercise huge amount of test vectors, low level of real-time observability and controllability of signals inside the prototype is a big issue. Various Design for Debug (DFD) techniques aim to improve the observability of signals and expedite the root cause analysis of errors. Typical practical DFD approaches are based on the Embedded Logic Analysis (ELA), using a trigger unit that can effectively control when to acquire the debug data. In this paper, we propose a hierarchical trigger generator that builds a trigger unit. Additionally, it provides resourceful and compact trace information for root cause analysis. Major advantages over traditional trigger units are: 1) by keeping the trace of interactions that leads to the failure, it facilitates the process of failure localization and root-cause analysis 2) it can be tuned for the specific location of a design to avoid the huge cost related to interfacing with trace signals 3) it can get parameterized to generate several units that can be placed inside the limited area in multiple debug rounds using a time-multiplex fashion.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".