Two-level configuration for FPGA: A new design methodology based on a computing fabric
Bibliographic record
Abstract
Large FPGAs require more and more time and expertise to efficiently target custom applications. This paper presents a new methodology based on two configuration levels. At the lowest level, the architecture is fully synthesized, placed and routed by experts to implement a 2-D mesh architecture of configurable algorithmic token machines. At the highest level, the users can program those machines to implement custom processing and routing. The architecture is data driven. The operations are triggered by the arrival of operands, leading to a large and functional pipeline spread over the whole FPGA. This methodology enables the fast implementation of data processing algorithms by people who are not experts in FPGA design, while achieving higher performances than a pure software solution. Two simple examples (FIR and FFT) illustrate the proposed methodology and demonstrate how it is possible to benefit from the expertise encapsulated at low level by just configuring the high level. Another advantage of the proposed methodology is the opportunity to dynamically reconfigure the fabric very quickly to best match the computation requirements at run time.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".