A Microwave Photonic Processor for Convolutional Neural Networks With Increased Effective Speed of Convolution
Bibliographic record
Abstract
Due to the strong feature extraction capabilities, convolutional neural networks (CNNs) have been utilized for various tasks, including image recognition, object detection, and natural language processing. The primary computational demand of CNNs stems from the convolution operations. In this paper, we propose a novel microwave photonic processor to accelerate the convolution operations in a CNN by increasing the effective speed of convolution. Thanks to the novel system architecture and the associated serialization approach, the effective speed is increased. Specifically, for a CNN with an M×M kernel size, the effective speed is increased by M times. The proposed processor is experimentally tested in which the MNIST and Fashion MNIST datasets are employed for its performance evaluation. The increase in the effective speed of convolution is experimentally confirmed. A computing speed of 102.4 giga operations per second (GOPS) with a root mean squared error (RMSE) of 0.0110 is demonstrated. In addition, the accuracies for the MNIST and Fashion MNIST image classification tasks are 98% and 88%, 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".