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Proceedings Paper

Electro- and all-optical photonic neural networks: towards real-time processors (Conference Presentation)
Author(s): Volker J. Sorger

Paper Abstract

While the data throughput of electronic processors is rather high (TFLOP range), the question arises whether optical neural networks (NN) enable a value proposition for zero-delay (real-time) processing? The answer might be ‘yes’, since once the network is trained, the time to perform an inference tasks is simply given by the time-of-flight of the photon through the processor’s NN. Here we discuss how the three functions of the perceptron (dot-product synaptic weighting, summation, and nonlinear thresholding) can be mapped onto a) optoelectronic [George et al. Opt.Exp. 2019], and b) all-optical hardware [Miscuglio et al. OMEx 2018]. The latter is realized via co-integration of phase-change-materials atop Silicon photonics. Once trained, the weights only require rare updating, thus saving power. Performance wise, such an integrated all-optical NN is capable of < fJ/MAC using experimental demonstrated pump-probe [Waldecker et al, Nat. Mat. 2015] with a delay per perceptron being ~ps.

Paper Details

Date Published: 9 March 2020
Proc. SPIE 11274, Physics and Simulation of Optoelectronic Devices XXVIII, 112740G (9 March 2020); doi: 10.1117/12.2547503
Show Author Affiliations
Volker J. Sorger, The George Washington Univ. (United States)

Published in SPIE Proceedings Vol. 11274:
Physics and Simulation of Optoelectronic Devices XXVIII
Bernd Witzigmann; Marek Osiński; Yasuhiko Arakawa, Editor(s)

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