  In this work we numerically analyze a passive photonic integrated
neuromorphic accelerator based on hardware-friendly optical spectrum slicing
nodes. The proposed scheme can act as a fully analogue convolutional layer,
preprocessing information directly in the optical domain. The proposed scheme
allows the extraction of meaningful spatio-temporal features from the incoming
data, thus when used prior to a simple fully connected digital single layer
network it can boost performance with negligible power consumption. Numerical
simulations using the MNIST dataset confirmed the acceleration properties of
the proposed scheme, where 10 neuromorphic nodes can replace the convolutional
layers of a sophisticated LeNet-5 network, thus reducing the number of total
floating point operations per second (FLOPS) by 98% while offering a 97.2%
classification accuracy.
