A Shared Synapse Architecture for All-Optical Spiking Neural Networks
Bibliographic record
Abstract
Silicon photonics is a promising technology for the development of neuromorphic hardware and Spiking Neural Networks (SNN). These architectures rely on wavelength division multiplexing (WDM) and precise calibration of microring resonators (MRR). Implementing larger neural network models requires an increasingly larger number of MRRs and this makes the calibration process complex and untenable. We propose a shared synapse architecture to reduce the number of MRRs required to perform synaptic weighting. This architecture reduces the number of required MRRs by half. The attenuation on each phase-change material (PCM) cell is derived from the pre-trained weights of the model without the need for retraining. To this end, a flow for assigning wavelengths to the inputs and finding the desired attenuation for each waveguide with PCM cell is introduced. Our simulation tests suggest similar weighting accuracy compared with the baseline model despite using fewer resources.
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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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".