Three dimensional waveguide-interconnects for scalable integration of photonic neural networks

TL;DR

This paper demonstrates the use of 3D printed photonic waveguides with fractal topology to create scalable, large-scale photonic interconnects for neural networks, overcoming size limitations of traditional 2D approaches.

Contribution

It introduces 3D printed photonic waveguides with fractal couplers and functional circuits for scalable neural network interconnects, enabling linear scaling of footprint area.

Findings

3D printed waveguides enable scalable interconnects

Fractal topology improves coupling efficiency

Functional circuits mimic deep convolutional neural networks

Abstract

Photonic waveguides are prime candidates for integrated and parallel photonic interconnects. Such interconnects correspond to large-scale vector matrix products, which are at the heart of neural network computation. However, parallel interconnect circuits realized in two dimensions, for example by lithography, are strongly limited in size due to disadvantageous scaling. We use three dimensional (3D) printed photonic waveguides to overcome this limitation. 3D optical-couplers with fractal topology efficiently connect large numbers of input and output channels, and we show that the substrate's footprint area scales linearly. Going beyond simple couplers, we introduce functional circuits for discrete spatial filters identical to those used in deep convolutional neural networks.