Hierarchical trigger generation for post-silicon debugging
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 post-silicon debugging process is aimed at locating design errors and electrical errors that concealed themselves during the whole process of pre-silicon verification. Although during 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 too big an issue for them. Various DFD techniques have come to improve observability of signals and expedite root cause analysis. Recently, typical practical DFD approaches are based on the Embedded Logic Analysis ELA. Since ELA has limitation in terms of the amount of data that can acquire in a debug experiment, we have to either increase the size of trace buffer or try to use trigger unit that can effectively control when to acquire the debug data. In this paper, we propose ZiMH a trigger generator that builds trigger unit. Additionally, it provides resourceful trace information for root cause analysis. Major advantages of generated trigger unit over traditional trigger units are: 1) it facilitates failure localization and root-cause analysis by keeping the trace of interaction that leads to the failure 2) it can be tuned for specific location to avoid the huge cost related to interfacing with trace signals 3) it can get parameterized to generate several trigger units that can be placed inside the limited area.
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