Bibliographic record
Abstract
As integrated circuits encapsulate more functionality and complexity, verifying that these devices operate correctly under all scenarios is an increasingly difficult task. Rather than using traditional verification techniques such as software simulation, more and more designers are taking advantage of the significantly higher clock speeds that can be achieved by using field-programmable gate-array (FPGA)-based prototypes. A key challenge to these prototypes is the lack of on-chip observability during debugging; one popular solution is to insert trace-buffers into the design to record a limited set of internal signals, but modifying this trace configuration often requires the entire circuit to be recompiled. In this paper, we propose that the original circuit mapping is fully preserved and incremental techniques are used to eliminate the need for a full recompilation, thereby accelerating the debugging process. By exploiting two opportunities available during trace-insertion: the ability to connect from any point of a signal to any trace-pin, and the internal symmetry of the FPGA architecture, we find that incremental trace-insertion can be 98 times faster than a full recompilation, return a routing solution with a shorter wirelength, and have a negligible effect on the critical-path delay of the original circuit when reclaiming 75% of the leftover memory capacity for tracing.
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 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 | 0.003 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".