Transistor‐Level Activation Functions via Two‐Gate Designs: From Analog Sigmoid and Gaussian Control to Real‐Time Hardware Demonstrations
Bibliographic record
Abstract
Abstract Tunable analog activation functions are essential for energy‐efficient artificial intelligence (AI) hardware. Two transistor designs are presented: the sigmoid‐like activation function transistor (SA‐transistor) and the Gaussian‐like activation function transistor (GA‐transistor), which implement analog sigmoid and Gaussian functions using a screen gate structure. In the SA‐transistor, adjusting the screen gate voltage ( V Screen‐G ) enables precise control of the sigmoid slope and saturation level. In the GA‐transistor, the amplitude and standard deviation of the Gaussian response are tunable through the same mechanism. These transistors enable precise and continuous tuning of analog activation parameters such as slope, amplitude, and width at the device level. This controllability allows hardware‐optimized neural computations tailored to specific tasks or datasets. Applied in real‐world tasks, the SA‐transistor improved lung magnetic resonance imaging (MRI) classification accuracy from 77% to 84%, and the GA‐transistor raised the time‐series forecasting coefficient of determination ( R 2 ) from 0.82 to 0.93. Furthermore, by assembling these devices into a hardware‐based multilayer perceptron (MLP), robust inference is demonstrated on the IRIS dataset with 96.7% overall accuracy. This system‐level validation highlights that analog activation transistors can directly support neuromorphic accelerators without digital post‐processing, reducing circuit complexity and power consumption while maintaining high classification fidelity.
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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 | 0.001 | 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".