VersaPower: Power estimation for diverse FPGA architectures
Bibliographic record
Abstract
This paper presents VersaPower, a tool capable of modelling the power usage of many different field programmable gate array (FPGA) architectures.The latest release of the academic FPGA CAD tool, Versatile Place and Route 6.0 (VPR), supports new architecture features such as fracturable look-up tables and complex logic blocks. Past FPGA power models do not support these new features. VersaPower is designed to work closely with VPR to provide power estimation for any architecture supported by this new CAD flow. This allows researchers to investigate the effects on power usage of both new FPGA architectures, as well as new CAD algorithms. VersaPower is designed to operate with modern CMOS technologies, and is validated against SPICE using 22 nm, 45 nm and 130 nm technologies. Results show that for common architectures, roughly 60% HDL of power consumption is due to the routing fabric, 30% from logic blocks and 10% from the clock network. Architectures ODN supporting fracturable LUTs require 5-10% more power, as each CLB has additional I/O pins, increasing the sizes of local interconnect crossbars and connection boxes.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.007 | 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".