A modular mixed-signal CVNS neural network architecture
Bibliographic record
Abstract
In this paper design and implementation of a modular mixed-signal feed-forward neural network is presented. The network is implemented based on the Continuous Valued Number System (CVNS) arithmetic with neurons distributed in the network. Synapse weights are implemented on the chip using capacitive analog memories. Weight values are stored as the CVNS values and are refreshed and updated using the overlap between the CVNS digits. Current-mode logic is used for implementation in order to simplify the circuit design, and especially addition, which resulted in reduced power and area consumption. The distributed nature of the neurons allows for expansion of the network into larger networks. Individual modular layers are fabricated in TSMC CMOS 180nm, and are used to form different network sizes. The module is used to configure two proof of concept examples, a 2 - 2 - 1 and a 3 - 2 - 1 network to solve the XOR problem. Results of test and verification presented in this paper show the network flexibility of the proposed design to form various network configurations.
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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".