Intelligent Meta-Imagers for Machine Vision

Rapid developments in machine vision have led to advances in a variety of industries, from medical image analysis to autonomous systems. These achievements, however, typically necessitate digital neural networks with heavy computational requirements, which are limited by high energy consumption and further hinder real-time decision-making when computation resources are not accessible. Here, we demonstrate an intelligent meta-imagers that are designed to work in concert with a digital back-end to off-load computationally expensive convolution operations into high-speed and low-power optics. The key to these architectures are the new freedoms afforded by metasurfaces such as optical edge isolation, polarization discrimination, and the ability to spatially multiplex, and demultiplex, information channels. I will discuss how these freedoms can be utilized for accelerating optical segmentation networks and objection classifiers, both based on incoherent illumination. This approach could enable compact, high-speed, and low-power image and information processing systems for a wide range of applications in machine-vision and artificial intelligence.