Presentation
9 March 2020 All-optical photonic integrated neural networks: a first take (Conference Presentation)
Author Affiliations +
Proceedings Volume 11299, AI and Optical Data Sciences; 112990G (2020) https://doi.org/10.1117/12.2546930
Event: SPIE OPTO, 2020, San Francisco, California, United States
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.
Conference Presentation
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mario Miscuglio, Teo Ting Yu, Armin Mehrabian, Robert Simpson, and Volker J. Sorger "All-optical photonic integrated neural networks: a first take (Conference Presentation)", Proc. SPIE 11299, AI and Optical Data Sciences, 112990G (9 March 2020); https://doi.org/10.1117/12.2546930
Advertisement
Advertisement
KEYWORDS
Neural networks

Picosecond phenomena

Electro optics

Thermography

Detection and tracking algorithms

Integrated photonics

Nonlinear optics

Back to Top