Leveraging reconfigurability to raise productivity in FPGA functional debug
Bibliographic record
Abstract
Abstract—We propose new hardware and software techniques for FPGA functional debug that leverage the inherent reconfigurability of the FPGA fabric to reduce functional debugging time. The functionality of an FPGA circuit is represented by a programming bitstream that specifies the configuration of the FPGA’s internal logic and routing. The proposed methodology allows different sets of design internal signals to be traced solely by changes to the programming bitstream followed by device reconfiguration and hardware execution. Evidently, the advantage of this new methodology vs. existing debug techniques is that it operates without the need of iterative executions of the computationally-intensive design re-synthesis, placement and routing tools. In essence, with a single execution of the synthesis flow, the new approach permits a large number of internal signals to be traced for an arbitrary number of clock cycles using a limited number of external pins. Experimental results using commercial FPGA vendor tools demonstrate productivity (i.e. run-time) improvements of up to 30 × vs. a conventional approach to FPGA functional debugging. These results demonstrate the practicality and effectiveness of the proposed approach. I.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".