Bibliographic record
Abstract
The cost functions used to evaluate logic synthesis transformations for FPGAs are far removed from the final speed and routability determined after placement, routing and timing analysis. This distance has given rise to the field of physical synthesis, which attempts to improve logic synthesis by employing cost functions that contain placement, routing and/or timing analysis information.In this work we take this notion to an extreme that we call omniscience, in which post-routing timing analysis is provided in the context of a manual editor in which the user selects logical and physical transformations. After each incremental circuit modification, the user is informed of the circuit performance after routing and timing analysis. Since the computations involved in providing this level of information are large, we restrict the application to relatively small circuits, no larger than 1000 logic elements.Using this approach on a commercial FPGA, we propose a set of logic transformations specific to the logic and routing architecture of the Xilinx Virtex-E device. On a set of 10 circuits we have achieved an average performance improvement of 10% when both logical and physical changes are used. Another value of the editor is that it reveals new types of automatable physical-synthesis transformations and optimization strategies that arise from architectural properties of the target device.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.015 |
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".