Dual‐Mode Optoelectronic Neuromorphic Memory for Complex Edge Detection and Recognition
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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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".