Leakage power reduction in FPGA DSP circuits through algorithmic noise tolerance
Bibliographic record
Abstract
We apply algorithmic noise-tolerance (ANT) techniques [1] to improve the energy efficiency of DSP circuits implemented on FPGAs. Our approach leverages the programmable power architectural feature in Altera commercial FPGAs that allows internal logic blocks to operate in two modes [2]: high speed or low (leakage) power. We build a main DSP circuit with high utilization of low-power mode blocks, reducing its speed and introducing errors into its output. The errors are subsequently (partially) corrected by a second (smaller) estimation circuit, producing an overall system with higher performance accuracy and lower power than a baseline system built with high-speed logic on its timing-critical paths. We demonstrate a filter implemented in a 40nm commercial FPGA that incorporates ANT and achieves higher SNR using 15% less static power than a traditional filter (without ANT).
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".