Neuromorphic convolution with a spiking DFB-SA laser neuron based on rate coding.

ABSTRACT

We propose a neuromorphic convolution system using a photonic integrated distributed feedback laser with a saturable absorber (DFB-SA) as a photonic spiking neuron. The experiments reveal that the DFB-SA laser can encode different stimulus intensities at different frequencies, similar to biological neurons. Based on this property, optical inputs are encoded into rectangular pulses of varying intensities and injected into the DFB-SA laser, enabling the convolution results to be represented by the firing rate of the photonic spiking neuron. Both experimental and numerical results show that the binary convolution is successfully achieved based on the rate-encoding properties of a single DFB-SA laser neuron. Furthermore, we numerically predict 4-channel quadratic convolution and accomplish MNIST handwritten digit classification using a spiking DFB-SA laser neuron model with rate coding. This work provides a novel approach for convolution computation, indicating the potential of integrating DFB-SA laser into future photonics spiking neural networks.