Scalability of all-optical neural networks based on spatial light modulators

GAEA / GAEA-2 Spatial Light Modulators
Imaging/ Image Processing Misc.: Speckle / Characterization / Metrology Optical Computing / Quantum Optics
Published on:
Authors: Ying Zuo, Zhao Yujun, You-Chiuan Chen, Shengwang Du & Liu, Junwei
Abstract:
“Optical implementation of artificial neural networks has been attracting great attention due to its potential in parallel computation at speed of light. Although all-optical deep neural networks (AODNNs) with a few neurons have been experimentally demonstrated with acceptable errors re- cently, the feasibility of large scale AODNNs remains unknown because error might accumulate inevitably with increasing number of neurons and connections. Here, we demonstrate a scalable AODNN with programmable linear operations and tunable nonlinear activation functions. We ver- ify its scalability by measuring and analyzing errors propagating from a single neuron to the entire network. The feasibility of AODNNs is further confirmed by recognizing handwritten digits and fashions respectively.”

Open Access

Publication: Physical Review Applied
Issue/Year: Physical Review Applied, 2021
DOI: https://doi.org/10.1103/PhysRevApplied.15.054034
Link: https://doi.org/https://doi.org/10.1103/PhysRevApplied.15.054034

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