Photonics Research, Volume. 9, Issue 8, 1446(2021)
Photonic extreme learning machine by free-space optical propagation
Fig. 1. Schematic architecture of the photonic extreme learning machine (PELM). (a) General ELM scheme with the input data set
Fig. 2. Learning ability of the PELM architecture. The optical computing scheme is evaluated on the MNIST data set by varying the encoding properties and feature space. (a) Input digit and 2D phase mask showing its encoding by noise embedding: the input signal overlaps with a disordered matrix. PELM training and testing error for noise embedding when varying the (b) noise amplitude and (c) its correlation length, for
Fig. 3. Experimental implementation. (a) Sketch of the optical setup. A phase-only spatial light modulator (SLM) encodes data on the wavefront of a 532 nm continuous-wave laser. The far field in the lens focal plane is imaged on a camera. Insets show a false-color embedding matrix and training data encoded as phase blocks, respectively. (b) Detected spatial intensity distribution for a given input sample. White-colored areas reveal camera saturation in high-intensity regions, which provides the network nonlinear function. Pink boxes show some of the
Fig. 4. Experimental performance of the PELM on classification and regression tasks. Confusion matrices on the MNIST data set for a free-space PELM, which makes use of (a) Fourier and (b) random embedding (92.18% and 92.06% accuracy,
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Davide Pierangeli, Giulia Marcucci, Claudio Conti. Photonic extreme learning machine by free-space optical propagation[J]. Photonics Research, 2021, 9(8): 1446
Category: Instrumentation and Measurements
Received: Feb. 25, 2021
Accepted: May. 23, 2021
Published Online: Aug. 11, 2021
The Author Email: Davide Pierangeli (davide.pierangeli@roma1.infn.it)