Efficient mixed-signal synapse multipliers for multi-layer feed-forward neural networks
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Bibliographic record
Abstract
An area and power-efficient modular mixed-signal synapse architecture is proposed for VLSI implementation of the multi-layer feed-forward neural network. The proposed circuitry multiplies synaptic weights that are stored in digital registers with the analog input. The multiplication result is always an analog current. Despite conventional MDACs principle in which all the multiplication work is performed based on weighted current mirrors, our structure performs the multiplication partially by small-area gates. This approach decreases the need for weighted current mirrors and lowers the size of transistors significantly. Modularity feature of the proposed circuit in combination with the scalable S-shaped neuron makes the structure capable of being easily adapted for various network configurations. This feature and the area-efficient multiplier design make the circuit an excellent choice to be used in large size multi-layer neural networks. The circuit is implemented in TSMC CMOS 0.18μm. The power at the maximum input level is 244um <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The area is 0.23mW with the measured output current error of less than 0.5μA.
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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 it