Current-Mode Analog Adaptive Mechanism for Ultra-Low-Power Neural Networks
Bibliographic record
Abstract
Neural networks (NNs) implemented at the transistor level are powerful adaptive systems. They can perform hundreds of operations in parallel but at the expense of a large number of building blocks. In the case of analog realization, an extremely low chip area and low power dissipation can be achieved. To accomplish this, the building blocks should be simple. This brief presents a new current-mode low-complexity flexible adaptive mechanism (ADM) with a strongly reduced leakage in analog memory. Input signals ranging from 0.5 to 20 μA are held for 10-50 ms, with the leakage rate from 0.2%/ms to 0.04%/ms, respectively, depending on temperature. A small storage capacitor of 200 fF enables a short write time (2when realized in the Taiwan Semiconductor Manufacturing Company Ltd. CMOS 0.18-μm technology. The potential application of this NN is envisioned in a mobile platform based on a wireless sensor network to be used for online analysis of electrocardiography signals.
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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) | 0.003 | 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".