Bibliographic record
Abstract
The demand for internet and computing resources has led to datacenters and servers being one of the fastest growing consumers of power in the world today. While datacenter power architectures have improved over time, the majority of the loss still occurs at the server power supply, and board level voltage regulators. To attempt to improve this, Google has proposed and implemented a 48 volt server architecture that can significantly reduce both the upstream conversion losses, and the distribution losses within the server racks. Google has estimated that this change can reduce their overall conversion losses by up to 30%, but to fully realize these benefits new technology is needed to convert 48 volts down to the point of load voltage levels. The Intermediate Bus Architecture, where a "bus converter" steps down the 48 volts to a lower bus voltage which is then stepped down by point-of-load regulators 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 proposed topology is a novel intermediate bus converter that can achieve 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, 99.2% peak efficiency, and 97.8% full load efficiency for 65A 12V output. Compared with other cutting edge designs this work achieves higher efficiency, and superior power density without the need for complex control, or a sensitive resonant based design.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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; both teacher heads agree on what is shown here.
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".