Progressive-BackSpace: Efficient Predecessor Computation for Post-Silicon Debug
Bibliographic record
Abstract
As microprocessors become more complex, finding errors in their design becomes more difficult. Most design errors are caught before the chip is fabricated, however, some make it into the fabricated design. One challenge in determining what is wrong with a new design after fabrication is the lack of observability into the state of the fabricated chip. To address this challenge, BackSpace proposes generating a trace of the states that lead up to an erroneous state. To add one state to the trace, BackSpace first generates a set of possible predecessor states (the pre-image), then tests them one at a time to find one that is reached during execution. In this paper, we propose an improved algorithm called Progressive-BackSpace. It does not enumerate every state in the pre-image. Instead, it first finds a reachable candidate state, and then determines if it is a predecessor state. This results in a practical implementation of BackSpace by greatly reducing the time needed to find prede- cessor states. The hardware overhead is also reduced by 94.4% relative to a recently proposed implementation of BackSpace. These algorithms were implemented and evaluated on a RTL model of an out-of-order processor, that models non-deterministic effects.
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.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".