Kite: A Family of Heterogeneous Interposer Topologies Enabled via Accurate Interconnect Modeling
Bibliographic record
Abstract
Recent advances in die-stacking and 2.5D chip integration technologies introduce in-package network heterogeneities that can complicate the interconnect design. Integrating chiplets over a silicon interposer offers new opportunities of optimizing interposer topologies. However, limited by the capability of existing network-on-chip (NoC) simulators, the full potential of the interposer-based NoCs has not been exploited. In this paper, we address the shortfalls of prior NoC designs and present a new family of chiplet topologies called Kite. Kite topologies better utilize the diverse networking and frequency domains existing in new interposer systems and outperform the prior chiplet topology proposals. Kite decreased synthetic traffic latency by 7% and improved the maximum throughput by 17% on average versus Double Butterfly and Butter Donut, two previous proposals developed using less accurate modeling.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".