Design of a Reconfigurable Activation Function for All-Optical Neural Networks
Bibliographic record
Abstract
Photonic integrated circuits present a promising avenue for the integration of Deep Neural Networks (DNNs), offering solutions to the speed and power consumption constraints inherent to their electronic counterparts. Notably, research showcasing the ability of photonic integrated circuits to realize matrix multiplications - a crucial operation in DNNs - at the speed of light has drawn much attention to the field of optical neural networks (ONNs). One of the challenges of designing fully optical DNNs is the photonic integration of the activation function, a nonlinear function. Optical nonlinear responses often deviate in shape from traditional DNN activation functions. A pivotal requirement for standardizing ONN architectures without sacrificing flexibility is the development of a fully tunable optical activation function. Presently, reconfigurable optical activation functions exhibit limitations in reproducing diverse functions, constraining the potential of photonic DNNs. In this article, we propose an architecture leveraging Mach-Zehnder interferometers and saturable absorbers to execute a range of activation functions, including ReLU, sigmoid, and tanh.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".