A Two-Phase Zero-Inductor Voltage Converter for Datacenter and Server Applications
Bibliographic record
Abstract
Sever power supplies and board level voltage regulators are currently the bottleneck limiting efficiency of overall server power architecture efficiency. In order to address this Google has proposed and implemented a 48 volt server architecture that brings a higher voltage level throughout the server rack to reduce distribution losses, and conversion losses upstream of the server power supply. However this change places additional burden on the board-level regulators that must now regulate from 48 volts down to below 1 volt for some components. The most common approach is a two stage approach called the "Intermediate Bus Architecture". A novel topology for an Intermediate Bus Architecture was proposed at APEC 2018. This topology featured extremely high efficiency, very simple control and low component count. However two other critical metrics for a bus converter are scability and power density. The work proposed in this paper demonstrates that the Zero-Inductor Voltage (ZIV) Converter can be easily scaled to multiple phases, and shows a very high density design while maintaining efficiency above other cutting edge solutions. The two-phase prototype achieves up to 800W/in3power density, 99.2% peak efficiency, and 97.9% full load efficiency for 840W output while maintaining good current sharing across all load conditions. The two-phase prototype requires no additional control, and is highly scalable to different power levels through paralleling.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".