A Microwave Photonic Neural Network in the Frequency Synthetic Dimension Using Multi-Tone Single-Sideband Modulation in a Fiber Loop
Bibliographic record
Abstract
Photonic synthetic dimension, an emerging concept in the field of photonics, introduces new dimensions within photonic systems, enabling more advanced and precise manipulation of high-speed optical signals. In this paper, we propose and experimentally demonstrate a novel microwave photonic neural network in the frequency synthetic dimension. By using a dual-parallel Mach-Zehnder modulator (DP-MZM) in an optical fiber loop, multi-tone carrier-suppressed single sideband (CS-SSB) modulation on multiple optical carriers is implemented which results in the dynamic coupling between different frequency modes in the loop, thus synthesizing additional frequency dimensions. In the synthetic dimension, the matrix-vector multiplication (MVM) between the neurons and the weights in a neural network is implemented by controlling the coupling coefficients between the optical carriers and the modulated sidebands, thus generating sidebands with specific intensities corresponding to the MVM results. By using a programmable optical filter after the DP-MZM to introduce attenuations and control the powers of the generated sidebands, the desired neuron biases can be also achieved. The multi-layer structure in a deep feedforward neural network (FNN) can be realized by refreshing the multi-tone radio frequency (RF) signals applied to the DP-MZM and the programmable filtering during each round trip in the optical loop. The approach is evaluated experimentally. A 3-2-8 three-layer FNN having 13 neurons is implemented to successfully tackle the task of 3-bit binary number recognition at a computational speed of 55×106 MAC/s (multiply-accumulate operations per second). An average prediction accuracy rate of 95.93% is demonstrated.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".