Silicon Photonic Filter-based Dot Product Engine for Convolutional Neural Networks

We present a silicon photonic filter-based analog engine for computing dot products in convolutional neural networks. It shows a greater energy efficiency compared to electronic solutions with a limited bit resolution degradation of input signals.