Bibliographic record
Abstract
Abstract With ever shrinking advanced CMOS nodes and evolution of systems with increasing complexity, the traditional SoC paradigm is facing extensive challenges in terms of yields and heterogeneity. The emerging industry solution to this has been to partition the SoCs into smaller units, with each unit performing a certain (though exclusive) function. This drove the birth of “chiplets”. With advent of chiplets, the traditional function of packaging as an after-thought to chip development has got a revolutionary face-lift. Packaging is now enabling interconnects to replace on-chip global interconnects. The onus now is on packaging to get the chiplets to integrate and communicate with each other such that the net performance is equivalent to or better than SoC. This has spawned a renaissance in field of semiconductor packaging, with newer multi-die packaging technologies being productized to realize newer and better interconnects. Some examples of these emerging technologies include advanced flip-chip, 2.5D, 2.1D, 3D, Wafer Level Fan-Out, and Bridge Technologies. AMD is at forefront of chiplet technologies, with extensive 7nm chiplet based product portfolio catering to the HPC market. This talk will discuss the current state of chiplet packaging technologies.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".