Bibliographic record
Abstract
FPGA CAD tools are often intended to compile an entire design from scratch to maximize the quality of results. Even with modern CAD algorithms this is a slow process. This paradigm limits designer productivity during incremental development, since every development iteration must endure the entire compilation process. Many vendor tools therefore offer ‘incremental modes’ that partially reuse compilation results to accelerate development at the HDL level of abstraction. Unfortunately, there is limited academic research into more sophisticated incremental HDL flows. We believe a key obstacle to research in this area is the lack of benchmarks which encapsulate realistic HDL development histories. As such we introduce Chronbench, a suite of HDL benchmarks which encapsulate development history as a chronological series of synthesizable commits in a git repository. In addition to five such benchmarks we present a tool for converting a public repository into a into a Chronbench benchmark. Further, we synthesize, place, and route 170 commits in order to fully characterize the suite. Finally, we analyze the characterization data to produce some key insights about the relative magnitude of HDL development changes and observe that approximately half of real development commits do not significantly impact device utilization, indicating significant potential for reuse during HDL development.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".