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Record W2990725048 · doi:10.1109/ecce.2019.8912999

A Comparison of Multilevel "Zero Inductor-Voltage" Converters for Data Center Applications

2019· article· en· W2990725048 on OpenAlexaff
Samuel Webb, Tianshu Liu, Yan‐Fei Liu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsQueen's University
Fundersnot available
KeywordsConvertersInductorCapacitorElectrical engineeringPower (physics)Network topologyVoltageComputer sciencePower densityServerEnhanced Data Rates for GSM EvolutionElectronic engineeringTopology (electrical circuits)EngineeringTelecommunicationsComputer networkPhysics

Abstract

fetched live from OpenAlex

Datacenter power architectures have improved over time, but the majority of the loss still occurs at the server power supply and board level voltage regulators. Google has proposed and implemented a 48 volt server architecture that can reduce their overall conversion losses by up to 30%, but to fully realize these benefits new technology is needed to converter 48 volts down to the point of load voltage levels. The Intermediate Bus Architecture is a very attractive option to bridge the gap between the 48 volt architecture of cutting edge servers, and the existing 12 volt architecture. The family of converters presented in this paper are intermediate bus converters that have demonstrated up to 990W/in <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> power density, and higher than 99% peak efficiency for 48V to 12V conversion. Compared with other cutting edge designs this family of converters achieves high power density and efficiency without the need for complex control, or a sensitive resonant based design. This is achieved through low reliance on magnetic components, without the drawbacks traditionally associated with switched capacitor topologies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.976
Threshold uncertainty score0.521

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.058
GPT teacher head0.327
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations4
Published2019
Admission routes1
Has abstractyes

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