Hyperbolic tangent passive resistive-type neuron
Bibliographic record
Abstract
In this paper, design of a passive resistive-type neuron is proposed to generate the hyperbolic tangent function as the activation function. The proposed resistive-type neuron has the advantage of not needing any biasing voltage and therefore its power consumption is low. The neuron circuit is designed and simulated in 180 nm CMOS technology. The proposed neuron shows a good approximation with maximum error and average error from the ideal hyperbolic tangent function by 19.7% and 6.88% respectively. The power consumption of the proposed neuron is 62.5 μW while the standby power is zero. Also the proposed neuron is applied in a large neural network and the results shows good functionality. The pattern recognition neural network implemented using the proposed neuron is consumed 295 μW power that is approximately 59.86% less than the same network proposed with the previous analog hyperbolic tangent designed neuron.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".