Dual‐Mode Optoelectronic Neuromorphic Memory for Complex Edge Detection and Recognition
Why this work is in the frame
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Bibliographic record
Abstract
Abstract In‐sensor neuromorphic computing possesses great potential in in‐sensor edge computing for massive image data processing, but today optoelectronic devices cannot meet the requirements on multifunction and high‐precision computing. Here MoS 2 heterojunction‐based optoelectronic memory device is proposed that can integrate two modes, the dynamic (short‐term memory, STM) and non‐dynamic (long‐term memory, LTM) into one unit to efficiently execute image processing. In the STM mode driven by negative bias, the memory device exhibits huge memory capacity, which enables the device to possess 128 photoconductance states that can supply 7‐bit spatiotemporal feature encoding of reservoir computing. The LTM mode that the heterojunction is positive bias, the memory device with multiple stable photoconductance states can supply physically parallel and one‐step hardware convolution acceleration. Under the photoconductance modulation mechanism, the energy consumption for a single convolutional kernel operation is ≈1.6 fJ. This result demonstrates that the type of optoelectronic memory can achieve energy efficiency advantages as well as enabling accelerated convolutional computing, yielding an accuracy 100% for 26‐letter image classification. This work lays a significant foundation on emerging image sensors.
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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 it