Bibliographic record
Abstract
Moore's Law is slowing down and the associated costs are simultaneously increasing. These pressures have given rise to new approaches utilizing advanced packaging and integration such as chiplets, interposers, and$3\mathrm{D}$stacking. We first describe the key technology drivers and constraints that motivate chiplet-based architectures, exploring several product case studies to highlight how different chiplet strategies have been developed to address different design objectives. We detail multiple generations of chiplet-based CPU architectures as well as the recent addition of$3\mathrm{D}$stacking options to further enhance processor capabilities. Across the industry, we are still collectively in the relatively early days of advanced packaging and 3D integration. As silicon scaling only gets more challenging and expensive while demand for computation continues to soar, we anticipate the transition to a new generation of chiplet architectures that utilize increasing combinations of 2D, 2.5D, and 3D integration and packaging technologies to continue to deliver compelling SoC solutions. However, this next era for chiplet innovation will face a variety of challenges. We will explore many of these technical topics, which in turn provide rich research opportunities for the community to explore and innovate.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.013 |
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".