Three dimensional waveguide-interconnects for scalable integration of photonic neural networks
This addresses the scalability problem for photonic neural network integration, offering a potential solution for more efficient hardware.
The paper tackled the size limitation of 2D photonic interconnects for neural networks by using 3D printed photonic waveguides with fractal topology, achieving linear scaling of the substrate's footprint area and enabling functional circuits for deep convolutional neural networks.
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.