  We report experimentally and in theory on the detection of edge information
in digital images using ultrafast spiking optical artificial neurons towards
convolutional neural networks (CNNs). In tandem with traditional convolution
techniques, a photonic neuron model based on a Vertical-Cavity Surface Emitting
Laser (VCSEL) is implemented experimentally to threshold and activate fast
spiking responses upon the detection of target edge features in digital images.
Edges of different directionalities are detected using individual kernel
operators and complete image edge detection is achieved using gradient
magnitude. Importantly, the neuromorphic (brain-like) image edge detection
system of this work uses commercially sourced VCSELs exhibiting spiking
responses at sub-nanosecond rates (many orders of magnitude faster than
biological neurons) and operating at the telecom wavelength of 1300 nm; hence
making our approach compatible with optical communication and data-center
technologies. These results therefore have exciting prospects for ultrafast
photonic implementations of neural networks towards computer vision and
decision making systems for future artificial intelligence applications.
