Pioneering Chiplet Technology and Design for the AMD EPYC™ and Ryzen™ Processor Families : Industrial Product
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
For decades, Moore’s Law has delivered the ability to integrate an exponentially increasing number of devices in the same silicon area at a roughly constant cost. This has enabled tremendous levels of integration, where the capabilities of computer systems that previously occupied entire rooms can now fit on a single integrated circuit.In recent times, the steady drum beat of Moore’s Law has started to slow down. Whereas device density historically doubled every 18-24 months, the rate of recent silicon process advancements has declined. While improvements in device scaling continue, albeit at a reduced pace, the industry is simultaneously observing increases in manufacturing costs.In response, the industry is now seeing a trend toward reversing direction on the traditional march toward more integration. Instead, multiple industry and academic groups are advocating that systems on chips (SoCs) be "disintegrated" into multiple smaller "chiplets." This paper details the technology challenges that motivated AMD to use chiplets, the technical solutions we developed for our products, and how we expanded the use of chiplets from individual processors to multiple product families.
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