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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