In this paper, a new dual-training method for a time-delay reservoir computing (RC) system based on a single vertical-cavity surface-emitting laser (VCSEL) is proposed and demonstrated experimentally for the first time. The prediction performance of the RC system by using the dual-training method has been experimentally and numerically investigated. Here, the dual-training method is defined as performing a further RC based on the difference between the target value and the predicted value of the traditional single training. It is found that enhanced prediction performance of the RC system can be obtained by employing the dual-training method, compared to the traditional single training method. More specifically, the NMSE values of the RC system with the dual-training method applied can be improved to 760% compared with the single training method in experiments. Besides, the effects of injection power, bias currents, feedback strength, and frequency detuning are also considered. The proposed dual-training method is of great significance to the performance enhancement of the RC and has an important promotion effect on the application of the RC in the future.

Photonic neuromorphic computing has emerged as a promising approach to building a low-latency and energy-efficient non-von Neuman computing system. A photonic spiking neural network (PSNN) exploits brain-like spatiotemporal processing to realize high-performance neuromorphic computing. However, the nonlinear computation of a PSNN remains a significant challenge. Here, we propose and fabricate a photonic spiking neuron chip based on an integrated Fabry–Perot laser with a saturable absorber (FP-SA). The nonlinear neuron-like dynamics including temporal integration, threshold and spike generation, a refractory period, inhibitory behavior and cascadability are experimentally demonstrated, which offers an indispensable fundamental building block to construct the PSNN hardware. Furthermore, we propose time-multiplexed temporal spike encoding to realize a functional PSNN far beyond the hardware integration scale limit. PSNNs with single/cascaded photonic spiking neurons are experimentally demonstrated to realize hardware-algorithm collaborative computing, showing the capability to perform classification tasks with a supervised learning algorithm, which paves the way for a multilayer PSNN that can handle complex tasks.

As Moore’s law has reached its limits, it is becoming increasingly difficult for traditional computing architectures to meet the demands of continued growth in computing power. Photonic neural computing has become a promising approach to overcome the von Neuman bottleneck. However, while photonic neural networks are good at linear computing, it is difficult to achieve nonlinear computing. Here, we propose and experimentally demonstrate a coherent photonic spiking neural network consisting of Mach–Zehnder modulators (MZMs) as the synapse and an integrated quantum-well Fabry–Perot laser with a saturable absorber (FP-SA) as the photonic spiking neuron. Both linear computation and nonlinear computation are realized in the experiment. In such a coherent architecture, two presynaptic signals are modulated and weighted with two intensity modulation MZMs through the same optical carrier. The nonlinear neuron-like dynamics including temporal integration, threshold, and refractory period are successfully demonstrated. Besides, the effects of frequency detuning on the nonlinear neuron-like dynamics are also explored, and the frequency detuning condition is revealed. The proposed hardware architecture plays a foundational role in constructing a large-scale coherent photonic spiking neural network.

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