Multi-channel free space optical convolutions with incoherent light

Convolutional layers are a critical feature of modern neural networks and require significant computational resources. Recently, researchers have developed optical accelerators as a low-energy, high-bandwidth approach for performing large-scale convolutions. Existing approaches perform convolutions on only one input channel to one or more output channels. Here we develop an optical convolution approach that simultaneously convolves multiple input channels each with their own set of convolutional kernels onto multiple output channels. Our approach uses a microlens array to redirect light from a 2D light emitter array through convolutional kernels encoded on an amplitude mask onto a camera. We experimentally test our multi-channel free space optical convolution approach and evaluate its performance using ray-tracing simulations. This work solves a major constraint of existing optical convolutional approaches, as modern convolutional networks use large numbers of input and output channels in convolutional layers.