Computing Primitive of Fully VCSEL-Based All-Optical Spiking Neural Network for Supervised Learning and Pattern Classification

We propose computing primitive for an all-optical spiking neural network (SNN) based on vertical-cavity surface-emitting lasers (VCSELs) for supervised learning by using biologically plausible mechanisms. The spike-timing-dependent plasticity (STDP) model was established based on the dynamics of the...

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Vydáno v:	IEEE transaction on neural networks and learning systems Ročník 32; číslo 6; s. 2494 - 2505
Hlavní autoři:	Xiang, Shuiying, Ren, Zhenxing, Song, Ziwei, Zhang, Yahui, Guo, Xingxing, Han, Genquan, Hao, Yue
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.06.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms All-optical spiking neural network (SNN) Computation Computer architecture Computer Systems Firing pattern Lasers Machine learning Neural networks Neural Networks, Computer neuromorphic photonics Neuromorphics Neuronal Plasticity Neurons Optical pulses Optimization Pattern classification Pattern Recognition, Automated - methods Photonics Power consumption Semiconductor optical amplifiers spike-timing-dependent plasticity (STDP) Spiking Supervised learning Supervised Machine Learning Synapses - physiology Vertical cavity surface emission lasers Vertical cavity surface emitting lasers vertical-cavity surface-emitting laser (VCSEL)
ISSN:	2162-237X, 2162-2388, 2162-2388
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Shrnutí:	We propose computing primitive for an all-optical spiking neural network (SNN) based on vertical-cavity surface-emitting lasers (VCSELs) for supervised learning by using biologically plausible mechanisms. The spike-timing-dependent plasticity (STDP) model was established based on the dynamics of the vertical-cavity semiconductor optical amplifier (VCSOA) subject to dual-optical pulse injection. The neuron-synapse self-consistent unified model of the all-optical SNN was developed, which enables reproducing the essential neuron-like dynamics and STDP function. Optical character numbers are trained and tested by the proposed fully VCSEL-based all-optical SNN. Simulation results show that the proposed all-optical SNN is capable of recognizing ten numbers by a supervised learning algorithm, in which the input and output patterns as well as the teacher signals of the all-optical SNN are represented by spatiotemporal fashions. Moreover, the lateral inhibition is not required in our proposed architecture, which is friendly to the hardware implementation. The system-level unified model enables architecture-algorithm codesigns and optimization of all-optical SNN. To the best of our knowledge, the computing primitive of an all-optical SNN based on VCSELs for supervised learning has not yet been reported, which paves the way toward fully VCSEL-based large-scale photonic neuromorphic systems with low power consumption.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2162-237X 2162-2388 2162-2388
DOI:	10.1109/TNNLS.2020.3006263