Broadband Optoelectronic Synapse Enables Compact Monolithic Neuromorphic Machine Vision for Information Processing
Bibliographic record
Abstract
Abstract Traditional machine vision is suffering from redundant sensing data, bulky structures, and high energy consumption. Biological‐inspired neuromorphic systems are promising for compact and energy‐efficient machine vision. Multifunctional optoelectronics enabling multispectrum sensitivity for broadband image sensing, feature extraction, and neuromorphic computing are vital for machine visions. Here, an optoelectronic synapse is designed that enables image sensing, convolutional processing, and computing. Multiple synaptic plasticity triggered by photons can implement photonic computing and information transmission. Convolutional processing is realized by ultralow energy kernel generators fully controlled by photons. Meanwhile, the device shows the ability of conductance modulations under electronic stimulations that implement neuromorphic computing. For the first time, this two‐terminal broadband optoelectronic synapse enables front‐end retinomorphic image sensing, convolutional processing, and back‐end neuromorphic computing. The integrated photonic information encryption, convolutional image preprocessing, and neuromorphic computing capabilities are promising for compact monolithic neuromorphic machine vision 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".