High-Density Photonic Convolution Computing Enabled by a Soliton Microcomb and a Microdisk Mesh Array
Bibliographic record
Abstract
Convolutional neural networks (CNNs) accelerated by photonic computing have attracted significant attention due to its potential to overcome the speed, scalability, and energy limitations of electronic architectures. An optical frequency comb has been demonstrated as a promising light source for dense photonic computing, thanks to its ability to provide multiple evenly spaced and mutually coherent frequency channels. However, prior microcomb-based convolution kernel accelerators have faced challenges, including limited computing density and insufficient dynamic reconfigurability. In this paper, we demonstrate a photonic convolutional processor achieving a computing density of 48 TOPS/mm² by combining a packaged soliton microcomb as the light source with a chip-scale micro disk mesh structure as the computing core. The soliton source exhibits excellent frequency and intensity stability, with a relative standard deviation of 0.48% over 60 minutes. The micro-disk mesh structure aligns both physically and topologically with 2D convolution operations, enabling reconfigurable and signed convolutional computing. This photonic convolutional processor offers strong potential for integration into high-speed, low-power consumption edge AI platforms and lays the foundation for scalable, reconfigurable on-chip optical neural networks in future large-scale photonic computing systems.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".