  All-optical binary convolution with a photonic spiking vertical-cavity
surface-emitting laser (VCSEL) neuron is proposed and demonstrated
experimentally for the first time. Optical inputs, extracted from digital
images and temporally encoded using rectangular pulses, are injected in the
VCSEL neuron which delivers the convolution result in the number of fast (<100
ps long) spikes fired. Experimental and numerical results show that binary
convolution is achieved successfully with a single spiking VCSEL neuron and
that all-optical binary convolution can be used to calculate image gradient
magnitudes to detect edge features and separate vertical and horizontal
components in source images. We also show that this all-optical spiking binary
convolution system is robust to noise and can operate with high-resolution
images. Additionally, the proposed system offers important advantages such as
ultrafast speed, high energy efficiency and simple hardware implementation,
highlighting the potentials of spiking photonic VCSEL neurons for high-speed
neuromorphic image processing systems and future photonic spiking convolutional
neural networks.
