CAD Techniques for NoC-Connected Multi-CGRA Systems
Bibliographic record
Abstract
Coarse-grained reconfigurable arrays (CGRAs) are programmable hardware platforms that are a means of realizing application accelerators. A CGRA is a 2D array of configurable processing elements (PEs), that connect to one another through programmable interconnects. PEs and interconnects in CGRAs are configurable at the word level, as opposed to at the bit level in FPGAs. A network-on-chip (NoC) is an on-chip communications system comprising routers and links, where data is packet-switched. NoCs provide more scalable communication vs. traditional on-chip busses or crossbars. In this paper, we propose an NoC-connected multi-CGRA system, where multiple modest-sized CGRAs communicate using an NoC. We introduce CAD techniques that partition and place an application across the “distributed” NoC-connected CGRAs. We study the quality and runtime of the CAD techniques on a system having sixteen 4 × 4 CGRAs connected together using a 2D mesh NoC.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".