Light-based neural network processes signals with no imaging step

Researchers have moved beyond traditional imaging detection in neural networks by demonstrating a system that processes signals using a single-pixel detector. Published in Light: Science & Applications, the work details a novel spatiotemporal all-optical neural network architecture integrating time and space for enhanced computation. This configuration shifts the focus from spatial light intensity to effectively overcoming speed bottlenecks caused by 2D array detectors. Experimental studies confirm the system accelerates processing of real-world spatiotemporal lidar signals, enabling real-time reconstruction of multi-target moving scenes. Operating at gigahertz speeds and without relying on traditional 2D array detectors, the system achieves signal processing by utilizing a single-pixel detector to measure temporal…