Intelligent Multi-channel Meta-imagers for Accelerating Machine Vision
This addresses energy and real-time constraints in machine vision for applications like medical imaging and autonomous systems, though it is incremental as it builds on existing optical computing concepts.
The paper tackles the high computational and energy costs of digital neural networks in machine vision by introducing an intelligent meta-imager that offloads convolution operations to optics, achieving 98.6% accuracy on handwritten digits and 88.8% on fashion images.
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-imager that is designed to work in concert with a digital back-end to off-load computationally expensive convolution operations into high-speed and low-power optics. In this architecture, metasurfaces enable both angle and polarization multiplexing to create multiple information channels that perform positive and negatively valued convolution operations in a single shot. The meta-imager is employed for object classification, experimentally achieving 98.6% accurate classification of handwritten digits and 88.8% accuracy in classifying fashion images. With compactness, high speed, and low power consumption, this approach could find a wide range of applications in artificial intelligence and machine vision applications.