Ultra-Low Power SAR ADC Using Statistical Characteristics of Low-Activity Signals
Bibliographic record
Abstract
Low-activity signals, such as voice, electrocardiogram (ECG), and ultrasonic signals, in the Internet-of-Things applications have both posed unique challenges and offered special opportunities for modern analog-to-digital conversion. This article presents a new successive approximation register (SAR) analog-to-digital converter (ADC) search methodology, which is aimed at low-activity signals for reducing comparator activity and switching energy of digital-to-analog converter (DAC). By using statistical histogram information of the low-activity signals, two search solutions are proposed. The first solution is designed for some part of signal that has small difference between two adjacent samples, while the second solution is designed for that with large difference. To engage one suitable solution, the digital interval between two adjacent samples needs to be detected. In addition, a new DAC tactic is proposed to reduce the activity of DAC switches. Our simulated 10-bit SAR ADC for voice signals shows that by using our proposed method, the comparator activity is reduced by 62.09%, and the DAC switching energy is decreased by 85.90% compared to the monotonic method. In addition, the activity of DAC switches is further trimmed by 39.66% compared to the monotonic method.
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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) | 0.001 | 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".