Photonic extreme learning machine by free-space optical propagation

Abstract

Photonic brain-inspired platforms are emerging as novel analog computing devices, enabling fast and energy-efficient operations for machine learning. These artificial neural networks generally require tailored optical elements, such as integrated photonic circuits, engineered diffractive layers, nanophotonic materials, or time-delay schemes, which are challenging to train or stabilize. Here we present a neuromorphic photonic scheme - photonic extreme learning machines - that can be implemented simply by using an optical encoder and coherent wave propagation in free space. We realize the concept through spatial light modulation of a laser beam, with the far field that acts as feature mapping space. We experimentally demonstrated learning from data on various classification and regression tasks, achieving accuracies comparable to digital extreme learning machines. Our findings point out an optical machine learning device that is easy-to-train, energetically efficient, scalable and fabrication-constraint free. The scheme can be generalized to a plethora of photonic systems, opening the route to real-time neuromorphic processing of optical data.

12 pages, 4 figures

References in corpus (6)

• 11 TeraFLOPs per second photonic convolutional accelerator for deep learning optical neural networks
• Large-scale photonic Ising machine by spatial light modulation
• High performance photonic reservoir computer based on a coherently driven passive cavity
• Human action recognition with a large-scale brain-inspired photonic computer
• Classification of time-domain waveforms using a speckle-based optical reservoir computer
• Fast Generation and Detection of Spatial Modes of Light using an Acousto-Optic Modulator

Cited by in corpus (2)

• Photonic Extreme Learning Machine based on frequency multiplexing
• Integrated Random Projection and Dimensionality Reduction by Propagating Light in Photonic Lattices