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27 April 2020 Towards real-time processors: Electro- and all-optical photonic neural networks (Conference Presentation)
Volker J. Sorger
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Proceedings Volume 11401, Real-Time Image Processing and Deep Learning 2020; 1140103 (2020) https://doi.org/10.1117/12.2559076
Event: SPIE Defense + Commercial Sensing, 2020, Online Only
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.
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Volker J. Sorger "Towards real-time processors: Electro- and all-optical photonic neural networks (Conference Presentation)", Proc. SPIE 11401, Real-Time Image Processing and Deep Learning 2020, 1140103 (27 April 2020); https://doi.org/10.1117/12.2559076
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KEYWORDS
Neural networks
Integrated optics
Thermography
Picosecond phenomena
Artificial intelligence
Data processing
Machine learning
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