An Analog CVNS-Based Sigmoid Neuron for Precise Neurochips
Bibliographic record
Abstract
In this paper, the design and implementation of an analog sigmoid neuron is presented. The activation function of the proposed neuron is implemented based on the piecewise linear approximation in the analog domain. The proposed neuron provides the required accuracy that cannot be achieved in general by analog neural network implementations. General digital outputs of a sigmoid neuron are replaced with fewer analog digits of the continuous valued number system (CVNS), while at the same time maximum approximation error is kept the same as the digital architectures. The proposed CVNS neuron resulted in an optimal ASIC implementation and is suitable for neurochips with on-chip learning. The VLSI implementation of the neuron is carried out using current-mode circuits. The implementation results compare favorably with previously developed structures in terms of area, delay, and power consumption. The proposed neuron structure occupies 28% less area compared with the state-of-the-art methods and it has two times lower power × delay.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".