CGRA-ME 2.0: A Research Framework for Next-Generation CGRA Architectures and CAD
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
Coarse-grained reconfigurable arrays (CGRAs) belong to the family of configurable processing architectures that have recently attracted increasing interest for their adaptability and efficiency. Research on CGRA architectures and their associated CAD tools is typically conducted empirically, by modelling a CGRA fabric, mapping applications onto it, and then assessing performance, power, and/or area (PPA). In this paper, we describe an open-source framework for such CGRA research - CGRA-ME - CGRA modelling and exploration. We describe the recently released version 2.0 of CGRA - ME, which incorporates a new mapping approach, support for elastic CGRAs, floating point, predication, hybrid RISC-V+CGRA systems, and more.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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