Optimizing effective interconnect capacitance for FPGA power reduction
Bibliographic record
Abstract
We propose a technique to reduce the effective parasitic capacitance of interconnect routing conductors in a bid to simultaneously reduce power consumption and improve delay. The parasitic capacitance reduction is achieved by ensuring routing conductors adjacent to those used by timing critical or high activity nets are left floating - disconnected from either VDD or GND. In doing so, the effective coupling capacitance between the conductors is reduced, because the original coupling capacitance between the conductors is placed in series with other capacitances in the circuit (series combinations of capacitors correspond to lower effective capacitance). To ensure unused conductors can be allowed to float requires the use of tri-state routing buffers, and to that end, we also propose low-cost tri-state buffer circuitry. We also introduce CAD techniques to maximize the likelihood that unused routing conductors are made to be adjacent to those used by nets with high activity or low slack, improving both power and speed. Results show that interconnect dynamic power reductions of up to ~15.5% are expected to be achieved with a critical path degradation of ~1%, and a total area overhead of ~2.1%.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".