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

Integrated Photonic Accelerator Based on Optical Spectrum Slicing for Convolutional Neural Networks