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.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".