On a New Mechanism of Trigger Generation for Post-Silicon Debugging
Bibliographic record
Abstract
The main goal of post-silicon debugging is to locate errors undetected during the pre-silicon verification. Although high speed of hardware prototype can be leveraged to expedite running a large number of realistic test vectors, the low level of observability and controllability of signals inside a prototype is a big concern. Design for Debug (DFD) techniques aim to improve the observability of signals and speed up the root-cause analysis of errors. Incorporation of an Embedded Logic Analyzer (ELA) is introduced as one of the practical DFD techniques. An ELA contains a trigger unit that controls conditions for which trace signals should be captured in a buffer for post-processing. In this paper, we propose a tool to generate hierarchical triggers, providing compact trace information for root-cause analysis. Major advantages of our technique as a means to generate trigger units are: 1) failure localization and root-cause analysis is expedited by keeping the hierarchical trace of interactions leading to failures, 2) overlapped failure patterns can be found by mechanism which results in a 60-65% reduction in hardware overhead compared to the previously proposed method, 3) it can be parameterized to generate several units, making it possible to incorporate checkers into scarce silicon area and enabling on-chip debugging by means of time-multiplexing scheme.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".