Bibliographic record
Abstract
Silicon interconnect fabric (Si-IF) is a wafer-scale heterogeneous integration platform. This platform promotes a paradigm shift in system integration and packaging methods, providing a single hierarchy of integration between the dies and the platform. The Si-IF effectively replaces the interposer, package, and printed circuit board. A power delivery methodology for high power wafer-scale systems (expected to dissipate up to 50 kW of power) is proposed in this paper. The proposed methodology includes three distinct power distribution topologies that are compared in terms of power loss, thermal consideration, and manufacturability. Compatible applications for each topology are also discussed. The electrical model, IR drop, and Ldi/dt noise, of each power distribution topology, are extracted and compared. Assuming a load voltage of 1 V, the three topologies exhibit a total voltage drop of, respectively, 16.68 mV, 9.62 mV, and 12.28 mV, corresponding to, respectively, 1.67%, 0.96%, and 1.23%. Hierarchical integration of decoupling capacitors is also described to ensure low voltage ripple (<; 5%) at the point of load. The electrical models of the power distribution topologies are verified using FEM and SPICE simulations.
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 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".