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Proceedings Paper

Optical neural networks: from integrated photonics to free-space solutions (Conference Presentation)
Author(s): Volker J. Sorger

Paper Abstract

Here we discuss two different approaches to realize optical neural networks (NN). Part ‘A’ focuses on integrated-photonics solution to realize a perceptron using (i) optoelectronic components to perform the MAC operation and (ii) a more novel concept of an all-optical nonlinearity for thresholding and thermo-optical memory for zero-power synaptic weighting. The latter is unique since the processor’s delay simply depends on the time-of-flight of the photon through the NN, which can be in the picosecond range enabling applications in real-time signal processing. Part ‘B’ discusses our recent process of 4f-based Fourier-optical filtering towards convolution neural networks (CNN); this approach uses massive parallelism (10^6 channels) of digital-mirror-display (DMD) technology. Here the computational expensive convolution is simple realized as a multiplication in the Fourier domain via a lens. Our generation (Gen) 1 system shows 250 TMAC/s process capability at 8-bit resolution.

Paper Details

Date Published: 9 March 2020
PDF
Proc. SPIE 11284, Smart Photonic and Optoelectronic Integrated Circuits XXII, 1128406 (9 March 2020); doi: 10.1117/12.2547495
Show Author Affiliations
Volker J. Sorger, The George Washington Univ. (United States)


Published in SPIE Proceedings Vol. 11284:
Smart Photonic and Optoelectronic Integrated Circuits XXII
Sailing He; Laurent Vivien, Editor(s)

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