Impact of FPGA Architectures on Area and Performance of CGRA Overlays
Bibliographic record
Abstract
We investigate Coarse-Grained Reconfgurable Arrays (CGRAs) and synthesize them as overlays on Field-Programmable Gate Arrays (FPGAs), and consider the impact of the underlying FPGA architecture on the performance and area of the produced CGRA overlay. This work extends the open-source CGRA modelling and exploration framework, CGRA-ME, to allow for quick generation of vendor-specific CGRA FPGA-overlays. Performance and area are measured and compared to the naive case CGRA implementation, where the naive case does not attempt to leverage unique FPGA architectural features. Results show a significant improvement over the naive case when CGRA FPGA-overlays are created with the FPGA architecture in mind. By offering quick, architecture-specific CGRA FPGA-overlay generation through CGRA-ME, a designer can physically model these architectures on FPGA platforms, with improved performance and area when compared to a naive implementation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".