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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Published in:IEEE transaction on neural networks and learning systems Vol. 32; no. 6; pp. 2494 - 2505
Main Authors: Xiang, Shuiying, Ren, Zhenxing, Song, Ziwei, Zhang, Yahui, Guo, Xingxing, Han, Genquan, Hao, Yue
Format: Journal Article
Language:English
Published: United States IEEE 01.06.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2020.3006263