Optimizing FPGA Logic Circuitry for Variable Voltage Supplies
Bibliographic record
Abstract
Unlike central processing units (CPUs), field-programmable gate arrays (FPGAs) have conventionally been powered with a fixed supply voltage (Vdd). However, recent efforts have shown that adopting dynamic voltage scaling reduces FPGA power consumption significantly. In this article, we analyze the delay sensitivity of different FPGA circuit elements to supply voltage changes and determine that conventional lookup table (LUT) designs greatly impact variable Vdd operation. To build FPGAs with lower delay sensitivity to Vdd, we propose several new LUT designs, including gate boosting the LUT, decoding the slowest two inputs of the LUT, and using separate voltage islands for the FPGA LUTs and routing. Our fastest proposed design (decode driver island) reduces the area-delay product of the FPGA logic plus routing tile compared to a conventional design by 12% and 52% at Vdd values of 0.8 V (the nominal voltage) and 0.6 V, respectively. Since our proposed FPGA tile designs are faster and have lower delay sensitivity to voltage, they offer better Energy-Delay2 product (ED2) than that of the baseline at nominal Vdd and below. Our decode-driver-island FPGA achieves a 26% ED2 reduction over the conventional design at the most efficient ED2 operating point.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".