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Proceedings Paper

All-optical photonic integrated neural networks: a first take (Conference Presentation)

Paper Abstract

If electro-optic conversion of current photonic NNs could be postponed until the very end of the network, then the execution time is simply the photon time-of-flight delay. Here we discuss a first design and performance of an all-optical perceptron and feed-forward NN. Key is the dual-purpose foundry-approved heterogeneous integration of phase-change-materials resulting in a) volatile nonlinear activation function (threshold) realized with ps-short optical pulses resulting in a non-equilibrium variation of the materials permittivity, and b) thermo-optically writing a non-volatile optical multi-cell (5-bit) memory for the NN weights after being (offline) trained. Once trained, the weights only required a rare update, 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 [Miscuglio et al. Opt.Mat.Exp. 2018] has a high cascadability.

Paper Details

Date Published: 9 March 2020
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Proc. SPIE 11299, AI and Optical Data Sciences, 112990G (9 March 2020);
Show Author Affiliations
Mario Miscuglio, The George Washington Univ. (United States)
Teo Ting Yu, Singapore Univ. of Technology and Design (Singapore)
Armin Mehrabian, The George Washington Univ. (United States)
Robert Simpson, Singapore Univ. of Technology and Design (Singapore)
Volker J. Sorger, The George Washington Univ. (United States)


Published in SPIE Proceedings Vol. 11299:
AI and Optical Data Sciences
Bahram Jalali; Ken-ichi Kitayama, Editor(s)

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