Optical Convolution

Optical convolution leverages the speed and parallelism of light to accelerate convolutional neural networks (CNNs), addressing the computational bottlenecks of traditional digital approaches in applications like machine vision and medical image analysis. Current research focuses on developing novel optical architectures, including free-space and on-chip implementations, often employing diffractive elements or metasurfaces to generate complex convolution kernels. These methods show promise in reducing power consumption and latency for CNN inference, particularly in edge computing and resource-constrained devices, while also enabling new possibilities in privacy-preserving imaging.