A Clockless Derivative-Dependent Sampling Scheme for Energy-Efficient IoT Applications
Bibliographic record
Abstract
This article presents a clockless nonuniform sampling scheme to enhance the energy efficiency of Internet of Things (IoT) applications. The proposed scheme employs a derivative-dependent mechanism that provides enhanced accuracy compared to other nonuniform sampling schemes while minimizing power consumption. By continuously monitoring the change in the derivative of the input signal, the proposed scheme identifies the most significant points of the signal, valuable for retention and conversion for effective signal reconstruction. In this scheme, the change in the derivative of the signal is compared to tunable threshold references, enabling adjustability to obtain the desired level of accuracy and adaptability to a variety of IoT applications. The proposed scheme is implemented in low- and high-speed systems that target low- and high-frequency applications, respectively. Fabricated using TSMC’s 0.13-$\mu $m CMOS technology, the performance is evaluated through experimental results in real-world scenarios. The proposed clockless derivative dependent sampling (CL-DDS) system can be integrated into the data acquisition system of an IoT device/sensor to save its critical power budget, while the threshold references are tuned to achieve the desired accuracy. The maximum power consumption of the proposed low- and high-speed CL-DDS designs is$1.15~\mu $W (@1 MHz) and$8.81~\mu $W (@20 MHz), respectively.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".