High‐Bit‐Efficiency TOPS Optical Tensor Convolutional Accelerator Using Microcombs
Bibliographic record
Abstract
Abstract Tensor convolution is a fundamental operation in convolutional neural networks, especially for processing tensors, which are prevalent in real‐world applications. Current methods often convert tensor convolutions into matrix multiplications, leading to data replication, additional memory usage and increased hardware complexity. Here, a high‐bit‐efficiency optical tensor convolution accelerator with reduced data redundancy and lower memory consumption is presented. The bit‐efficiency of the optical tensor convolution accelerator is first explored, significantly improving its effective computing power by utilizing the spatial dimension. Consequently, the optical tensor convolutional accelerator operates at speeds exceeding 3 Tera Operations Per Second (TOPS)—the fastest single‐kernel optical convolutional accelerator to date, to the best of authors' knowledge. Its performance is validated on handwritten digit recognition and histopathologic cancer detection tasks, achieving 93.8% and 77% accuracy, respectively, closely matching in‐silico results. This approach simultaneously multiplexes the physical dimensions—wavelength, time, and space—and leverages the parallelism and high throughput of light, enabling efficient optical processing of tensor data with significant computational power.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".