Limit Study of Energy & Delay Benefits of Component-Specific Routing
Bibliographic record
Abstract
As feature sizes scale toward atomic limits, parameter variation continues to increase, leading to increased margins in both delay and energy. The possibility of very slow devices on critical paths forces designers to increase transistor sizes, reduce clock speed and operate at higher voltages than desired in order to meet timing. With post-fabrication configurability, FPGAs have the opportunity to use slow devices on non-critical paths while selecting fast devices for critical paths. To understand the potential benefit we might gain from component-specific mapping, we quantify the margins associated with parameter variation in FPGAs over a wide range of predictive technologies (45nm–12nm) and gate sizes and show how these margins can be significantly reduced by delay-aware, component-specific routing. For the Toronto 20 benchmark set, we show that component-specific routing can eliminate delay margins induced by variation and reduce energy for energy minimal designs by 1.42–1.98×. We further show that these benefits increase as technology scales.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".