Incremental distributed trigger insertion for efficient FPGA debug
Bibliographic record
Abstract
FPGA-based prototyping enables evaluating complex designs directly in hardware, at speeds orders of magnitude faster than simulation. However, this approach suffers from the lack of observability during debugging. To enhance observability, designers insert debug instrumentation; trace buffers are used to record a small subset of data. Since these buffers have limited capacity, trigger circuits are required to start and/or stop recording based on the values of selected signals in the circuit. Although it is possible to insert trigger circuits at compile time, changing the trigger behaviour requires re-compiling the design, increasing the cost of each debug iteration. In this paper, we propose inserting trigger circuits at run-time by distributing trigger logic over spare resources of a fully placed-and-routed design such that its mapping is completely preserved. We also propose CAD optimizations which improve routability of the trigger circuitry, and minimize the impact on circuit delay. We find that using our techniques to implement the trigger logic can be an order of magnitude faster than a full recompilation.
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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".