An adaptive virtual overlay for fast trigger insertion for FPGA debug
Bibliographic record
Abstract
Field-programmable gate-array (FPGA) platforms are commonly used for prototyping complex designs, allowing designers to evaluate and validate the functionality at speeds that are orders of magnitude faster than simulation. To counter the limited observability of hardware, on-chip trace buffers are used to record the behaviour of a small subset of signals. To effectively use the limited capacity of these on-chip trace buffers, trigger circuitry is required to determine when to start and/or stop recording signal behaviour. Although it is possible to implement the trigger circuitry and add it to the user circuit at compile time, this would require recompiling a design every time the trigger circuit is modified, reducing debug productivity. In this paper, we present and evaluate an adaptive virtual overlay architecture for rapid trigger implementation. The overlay is built from logic and routing resources not used by the user circuit, reducing the overhead and impact on the user circuit. At debug time, the pre-synthesised overlay architecture can quickly be configured to implement the desired trigger functionality. We show that our overlay architecture provides flexibility required for mapping trigger circuitry with negligible impact on delay. We also show trigger mapping is significantly faster rather than recompile insertion, increasing debug productivity.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".