2022/01/26 by Hanyu Zheng, Zheng, Hanyu, Quan Liu +9 · 3 citations
Computer Science · Engineering · Materials Science · #FOS: Electrical engineering #FOS: Physical sciences #Image and Video Processing (eess.IV) #Metamaterials and Metasurfaces Applications #Neural Networks and Reservoir Computing #Optics (physics.optics) #Photonic and Optical Devices #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2201.11034
openalex publication_date 2022/01/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Rapid advances in deep learning have led to paradigm shifts in a number of fields, from medical image analysis to autonomous systems. These advances, however, have resulted in digital neural networks with large computational requirements, resulting in high energy consumption and limitations in real-time decision making when computation resources are limited. Here, we demonstrate a meta-optic based neural network accelerator that can off-load computationally expensive convolution operations into high-speed and low-power optics. In this architecture, metasurfaces enable both spatial multiplexing and additional information channels, such as polarization, in object classification. End-to-end design is used to co-optimize the optical and digital systems resulting in a robust classifier that achieves 95% accurate classification of handwriting digits and 94% accuracy in classifying both the digit and its polarization state. 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.