Analyzing and predicting the impact of CAD algorithm noise on FPGA speed performance and power
Bibliographic record
Abstract
FPGA CAD algorithms are heuristic, and generally make use of cost functions to gauge the value of one potential circuit implementation over another. At times, such algorithms must decide between two or more implementation options of apparently equal cost. This work explores the variations in circuit quality, i.e. noise, that arise when CAD algorithms are altered to choose randomly when faced with such equal-cost alternatives. Noise sources are identified in logic synthesis and technology mapping algorithms, and experimental results are presented which show standard deviations of 3.3% and 3.7% from the mean in post-routed delay and power. As a means of dealing with this variation, early timing and power prediction metrics can be applied after technology mapping to find the best circuits in the presence of noise. When applied to designs with over 1.5% variation in delay and power, the best prediction models have a 40% probability of capturing the best circuit when predicting the top 10% of circuits in a group of noise-injected circuits.
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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".