Optical Convolution Processing Based on an Amplified Fiber-Optic Recirculating Loop
Bibliographic record
Abstract
Convolution processing is a key function in convolutional neural networks (CNNs). To increase the computational speed of a CNN, optical convolution processing (OCP) can be employed to leverage the high speed and parallelism of light. However, the scalability of most OCP schemes is poor. For a given system, the kernel size is fixed, or the system must be expanded with more components to handle convolution processing with larger kernel sizes. Here, we propose an approach to implementing OCP capable of handling various kernel sizes based on an amplified fiber-optic recirculating loop without changing the configuration of the system. For an input data sequence, the multiplication of the input data with a kernel having$n$weights is implemented by applying the input data and the kernel weights to a MachZehnder modulator (MZM), to allow the weighted input data to recirculate in the amplified fiber-optic loop n-1 times. The weighted and time-delayed data are summed at a photodetector (PD), and the convolution operation is completed. A proof-ofconcept experiment is performed, in which the feature maps of handwritten digits from the MNIST dataset using different kernels at a speed of 16 giga operations per second (GOPS) are obtained.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".