  Deeplearning algorithms are revolutionising many aspects of modern life.
Typically, they are implemented in CMOS-based hardware with severely limited
memory access times and inefficient data-routing. All-optical neural networks
without any electro-optic conversions could alleviate these shortcomings.
However, an all-optical nonlinear activation function, which is a vital
building block for optical neural networks, needs to be developed efficiently
on-chip. Here, we introduce and demonstrate both optical synapse weighting and
all-optical nonlinear thresholding using two different effects in a
chalcogenide material photonic platform. We show how the structural phase
transitions in a wide-bandgap phase-change material enables storing the neural
network weights via non-volatile photonic memory, whilst resonant bond
destabilisation is used as a nonlinear activation threshold without changing
the material. These two different transitions within chalcogenides enable
programmable neural networks with near-zero static power consumption once
trained, in addition to picosecond delays performing inference tasks not
limited by wire charging that limit electrical circuits; for instance, we show
that nanosecond-order weight programming and near-instantaneous weight updates
enable accurate inference tasks within 20 picoseconds in a 3-layer all-optical
neural network. Optical neural networks that bypass electro-optic conversion
altogether hold promise for network-edge machine learning applications where
decision-making in real-time are critical, such as for autonomous vehicles or
navigation systems such as signal pre-processing of LIDAR systems.
