Synthesizable FPGA fabrics targetable by the Verilog-to-Routing (VTR) CAD flow
Bibliographic record
Abstract
We consider implementing FPGAs using a standard cell design methodology, and present a framework for the automated generation of synthesizable FPGA fabrics. The open-source Verilog-to-Routing (VTR) FPGA architecture evaluation framework [1] is extended to generate synthesizable Verilog for its in-memory FPGA architectural device model. The Verilog can be synthesized into standard cells, placed and routed using an ASIC design flow. A second extension to VTR generates a configuration bitstream for the FPGA; that is, the bitstream configures the FPGA to realize a user-provided placed and routed design. The proposed framework and methodology opens the door to silicon implementation of a wide range of VTR-modelled FPGA fabrics. In an experimental study, area and timing-optimized FPGA implementations in 65nm TSMC standard cells are compared with a 65nm Altera commercial FPGA.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".