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

• 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
• DOI: 10.1109/TNNLS.2020.3006263

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.