Real-time control with photonic neural networks
Bibliographic record
Abstract
Photonic computing, especially neuromorphic processors, offers high-throughput linear processing with a crucial advantage in achieving low-latency applications over electronics. While conventional electronics prioritize throughput at the expense of latency, photonic systems excel in low-latency applications. Historically, neuromorphic photonic systems focused on machine learning, overlooking their potential in real-time control scenarios. However, recent trends highlight AI's role in complex control tasks like autonomous navigation and scientific experiments demanding low-latency inference. This talk presents a framework for implementing photonic neural networks in control applications, emphasizing their relevance in Model Predictive Control (MPC) and reinforcement learning (RL). Simulations demonstrate the capability of a modest number of neurons to handle nonlinear control tasks, surpassing linear controllers. Furthermore, a spiking implementation of photonic neural networks can bring additional benefits to challenging control tasks requiring ultra-low latency.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".