Scaling the Area of Synthesizable FPGA Tiles Across Semiconductor Process Nodes
Bibliographic record
Abstract
Synthesizable field-programmable gate arrays (FPGAs) have recently gained significant traction due to their low development costs and their ability to adapt to new process technologies. The successful adoption of synthesizable FPGAs requires robust methodologies for estimating the area characteristics of the FPGA tiles in the synthesizable FPGA fabrics. FPGA tile area is used to determine the physical lengths of an FPGA’s routing segments and is therefore crucial to ensuring the accurate benchmarking of newly proposed FPGA architectures. In this work, we present a methodology to estimate the area of synthesizable FPGA tiles across various semiconductor process technologies. The methodology leverages scaling trends in the area of synthesizable FPGA tiles and selected hierarchical blocks to derive scaling factors that can be used to scale the area of synthesizable FPGA tiles across process nodes. The results demonstrate that this methodology achieves a maximum absolute percentage error of less than 10% when scaling the area of synthesizable FPGA tiles across open-sourced 130 nm, 45 nm, 15 nm and 7 nm process nodes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".