CGRA-ME: A unified framework for CGRA modelling and exploration
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) are a style of programmable logic device situated between FPGAs and custom ASICs on the spectrum of programmability, performance, power and cost. CGRAs have been proposed by both academia and industry; however, prior works have been mainly self-contained without broad architectural exploration and comparisons with competing CGRAs. We present CGRA-ME - a unified CGRA framework that encompasses generic architecture description, architecture modelling, application mapping, and physical implementation. Within this framework, we discuss our architecture description language CGRA-ADL, a generic LLVM-based simulated annealing mapper, and a standard cell flow for physical implementation. An architecture exploration case study is presented, highlighting the capabilities of CGRA-ME by exploring a variety of architectures with varying functionality, interconnect, array size, and execution contexts through the mapping of application benchmarks and the production of standard cell designs.
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 | 0.001 | 0.001 |
| 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