  Neuromorphic computing aims to mimic the architecture of the human brain to
carry out computational tasks that are challenging and much more energy
consuming for standard hardware. Despite progress in several fields of physics
and engineering, the realization of artificial neural networks which combine
high operating speeds with fast and low-energy adaptability remains a
challenge. Here we demonstrate an opto-magnetic neural network capable of
learning and classification of digitized 3x3 characters exploiting local
storage in the magnetic material. Using picosecond laser pulses, we find that
micrometer sized synapses absorb well below 100 picojoule per synapse per laser
pulse, with favorable scaling to smaller spatial dimensions. We thus succeeded
in combining the speed and low-dissipation of optical networks with the
low-energy adaptability and non-volatility of magnetism, providing a promising
approach to fast and energy-efficient neuromorphic computing.
