Optical Convolution Processing Based on an Amplified Fiber-Optic Recirculating Loop
Bibliographic record
Abstract
Convolution processing plays a pivotal role in convolutional neural networks (CNNs), which are widely utilized in image recognition, signal processing, and other applications that demand high computational speed and efficiency. To enhance the computational speed, optical convolution processing (OCP), which leverages the inherent parallelism and high speed of optical systems, has emerged as an effective solution. However, existing OCP implementations often encounter scalability challenges, as most systems are limited to fixed kernel sizes or require substantial hardware expansion to support larger kernels. In this work, 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 nn weights is implemented by applying the input data and the kernel weights to a Mach-Zehnder modulator (MZM), to allow the weighted input data to recirculate in the amplified fiber-optic loop nn-11 times. The weighted and time-delayed data are summed at a photodetector (PD), and the convolution operation is completed. The proposed system is evaluated by a proof-of-concept experiment, where convolution operations are performed on the MNIST and fashion-MNIST datasets to generate feature maps using kernels with different sizes. A computational speed of 16 giga operations per second (GOPS) with 92.8% and 73.6% classification accuracies are achieved, respectively.
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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) | 0.000 | 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 | 0.001 | 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".