Real-time control with photonic neural networks

Thomas Ferreira de Lima; Hugh Morison; Bhavin Shastri; Paul Prucnal

doi:10.1117/12.3020684

18 June 2024 Real-time control with photonic neural networks

Thomas Ferreira de Lima, Hugh Morison, Bhavin Shastri, Paul Prucnal

Proceedings Volume PC13017, Machine Learning in Photonics; PC130170F (2024) https://doi.org/10.1117/12.3020684
Event: SPIE Photonics Europe, 2024, Strasbourg, France

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.

Conference Presentation

Citation Download Citation

Thomas Ferreira de Lima, Hugh Morison, Bhavin Shastri, and Paul Prucnal "Real-time control with photonic neural networks", Proc. SPIE PC13017, Machine Learning in Photonics, PC130170F (18 June 2024); https://doi.org/10.1117/12.3020684

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