Energy Recovery from High-Frequency Clocks Using DC-DC Converters
Bibliographic record
Abstract
Large digital chips use a significant amount of energy to distribute a multi-GHz clock. By discharging the clock network to ground every cycle, the energy stored in this large capacitor is wasted. Instead, the energy can be recovered using an on-chip DC-DC converter. This paper investigates the integration of two DC-DC converter topologies, boost and buck-boost, with a high-speed clock driver. The high operating frequency significantly shrinks the required size of the L and C components so they can be placed on-chip; typical converters place them off-chip. The clock driver and DC-DC converter are able to share the entire tapered buffer chain, including the widest drive transistors in the final stage. To achieve voltage regulation, the must be modulated; implying only single-edge-triggered flops should be used. However, this minor drawback is eclipsed by the benefits: by recovering energy from the clock, the output power can actually exceed the additional power needed to operate the converter circuitry, resulting in an effective efficiency greater than 100%. Furthermore, the converter output can be used to operate additional power-saving features like low-voltage islands or body bias voltages.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".