Efficient mixed-signal synapse multipliers for multi-layer feed-forward neural networks
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 244um2. The area is 0.23mW with the measured output current error of less than 0.5μA.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".