Multi-Level Encoding and Decoding in a Scalable Photonic Tensor Processor With a Photonic General Matrix Multiply (GeMM) Compiler
Bibliographic record
Abstract
The resurgence of artificial intelligence enabled by deep learning and high performance computing has seen a dramatic increase of demand in the accuracy of deep learning model which has come at the cost of computational complexity. The fundamental operations in deep learning models are matrix multiplications, and large scale matrix operations and data-centric tasks have experienced bottlenecks from current digital electronic hardware in terms of performance and scalability. Recent research on photonic processors have found solutions to enable applications in machine learning, neuromorphic computing and high performance computing using basic photonic processing elements on integrated silicon photonic platform. However, efficient and scalable photonic computing requires an information encoding/decoding scheme. Here, we propose a multi-level encoding and decoding scheme, and experimentally demonstrate it with a wavelength-multiplexed silicon photonic processor. We also discuss the scalability of our proposed scheme by introducing a photonic general matrix multiply compiler, and consider the effects of speed, bit precision, and noise. Our proposed scheme could be adapted to a variety of photonic information processing architectures for photonic neural networks, photonics tensor cores, and programmable photonic.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".