  With the growing prevalence of AI, demand increases for efficient machine
learning hardware. Physical systems are sought which combine image feature
detection with the essential nonlinearity for tasks such as image
classification. Existing physical hardware typically detects features linearly,
then employs digital processing for nonlinear activation. Biological vision
systems excel at nonlinear image processing. The retina detects features in
ganglion cells via lateral inhibition, where cells nonlinearly compete for
neuronal firing while supressing neighbouring cells.
  We present a bio-inspired 'retinomorphic' machine vision platform using an
on-chip semiconductor network laser. The system detects multiple features in
parallel via spatially-overlapping lasing modes, with integrated nonlinearity
provided by antagonistic gain competition between modes - a photonic analogue
of retinal inhibition. Parallel feature-detection enhances efficiency relative
to feature-detection schemes which operate sequentially or via multiple device
copies, with Si-compatible processing and a compact micron-scale footprint
relative to existing mm-scale systems. We report 98.05% accuracy on
MNIST-digits and 87.85% on Fashion-MNIST, with strong performance on short
training datasets.
