Measuring the Power Efficiency Of Subthreshold FPGAs For Implementing Portable Biomedical Applications
Bibliographic record
Abstract
Power is a significant design constraint for implementing portable applications. Operating transistors in the subthreshold region can significantly reduce power consumption while reducing performance. The low frequency nature of biosignals makes a FPGA operating subthreshold region a good candidate. In this work, I investigate the feasibility of desinging such a device and the trade-off between power consumpation and performance for FPGA routing resources operating in the subthreshold region. For the 32nm Predictive Technology Model studied in this work, it was observed a power reduction of 197.7 times (or power-delay-product reduction of 3.3 times) for operating under a supply voltage of 0.4 volts (as compared to normal operation in the saturation region using a 0.9V). Under a supply voltage of 0,4 volts, FPGA can operate at 2.0 MHz while allowing signals to propagate unregistered through 20 routing tracks which meets the real-time requirement for processing 20000 samples per second.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".