Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3\mathrm{D}$</tex> 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 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3\mathrm{D}$</tex> 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.
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.
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) | 0.000 | 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 it