Bibliographic record
Abstract
In this paper, we present COFFE (Circuit Optimization For FPGA Exploration), a new fully-automated transistor sizing tool for FPGAs. Automated transistor-level CAD tools are an important part of the architecture exploration flow because they provide accurate area and delay estimates of low-level FPGA circuitry, which must be obtained for each architecture. We show that modeling transistors as linear resistances and capacitances as has been done in previous FPGA transistor sizing tools is highly inaccurate for fine-grained transistor-level design in advanced process nodes. Therefore, COFFE's transistor sizing algorithm maintains circuit non-linearities by relying exclusively on HSPICE simulations to measure delay. Area is estimated with a transistor size-based model that incorporates a number of improvements to enhance its accuracy in advanced process technologies versus prior methods. In addition to more accurate area and delay estimation, COFFE considers more layout effects than prior published work by automatically accounting for transistor and wire loads, which are computed based on architectural parameters and layout area. This new FPGA transistor sizing tool requires only several hours to produce high-quality transistor sizing results for an entire FPGA tile; a task that would normally take months of manual effort. We demonstrate COFFE's utility in FPGA architecture studies by investigating an important new architectural question at the logic-to-routing interface.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".