TOPS‐Speed Reconfigurable Photonic Transposed Convolution Accelerator for Generative Tasks
Bibliographic record
Abstract
ABSTRACT Transposed convolution, crucial in large‐parameter generative models ranging from content creation to autonomous driving, imposes substantial demands on GPU memory and energy consumption in electronic processors. Electronic processors, fundamentally limited by the von Neumann architecture and further hindered by silicon‐based quantum tunneling effects, struggle to meet the stringent real‐time requirements of modern generative workloads. In contrast, optical computing—exploiting ultra‐wide bandwidth and ultra‐low power consumption—offers a promising alternative for high‐speed transposed convolution in next‐generation AI. Here, we introduce a high‐speed and reconfigurable photonic transposed convolution accelerator (PTCA). By interleaving wavelength, temporal, and spatial dimensions and leveraging an integrated Kerr microcomb for data‐dimension expansion, the PTCA achieves tera operations per second (TOPS) with 100% bit efficiency. Experiments demonstrate a processing speed of 1.026 TOPS, making it, to the best of our knowledge, the fastest reconfigurable PTCA to date. In Fashion‐MNIST reconstruction tasks, this system achieves a mean squared error (MSE) of 0.0062 without any additional post‐processing by electronic fully connected layers. Our work thus establishes a high‐speed, reconfigurable photonic paradigm for accelerating future generative AI.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".