A High-Speed Microwave Photonic Processor for Convolutional Neural Networks
Bibliographic record
Abstract
High-speed convolutional neural networks (CNNs) play a pivotal role in tasks ranging from facial recognition and object detection to medical image analysis and classification. In this paper, we present and experimentally demonstrate a novel microwave photonic processor for convolution processing that is employed to accelerate CNNs. Thanks to the novel system architecture and the associated serialization approach, the effective speed is increased. Specifically, for a CNN with a 2x2 kernel, the effective speed is doubled and for a CNN with a 3x3 kernel, the effective speed is tripled. The processor is experimentally tested in which the MNIST dataset is employed for its performance evaluation. The results show various feature maps can be achieved at an increased speed. Our findings indicate that this microwave photonics processor can greatly enhance the speed and efficiency for convolution processing, underscoring its potential for widespread applications in high-demand computational tasks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".