Integrated photonic encoder for low power and high-speed image processing

Xiao Wang, Brandon Redding, Nicholas Karl, Christopher Long, Zheyuan Zhu, James Skowronek, Shuo Pang, David Brady () and Raktim Sarma ()
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Xiao Wang: University of Arizona
Brandon Redding: U.S. Naval Research Laboratory
Nicholas Karl: Sandia National Laboratories
Christopher Long: Sandia National Laboratories
Zheyuan Zhu: University of Central Floria
James Skowronek: University of Arizona
Shuo Pang: University of Central Floria
David Brady: University of Arizona
Raktim Sarma: Sandia National Laboratories

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract Modern lens designs are capable of resolving greater than 10 gigapixels, while advances in camera frame-rate and hyperspectral imaging have made data acquisition rates of Terapixel/second a real possibility. The main bottlenecks preventing such high data-rate systems are power consumption and data storage. In this work, we show that analog photonic encoders could address this challenge, enabling high-speed image compression using orders-of-magnitude lower power than digital electronics. Our approach relies on a silicon-photonics front-end to compress raw image data, foregoing energy-intensive image conditioning and reducing data storage requirements. The compression scheme uses a passive disordered photonic structure to perform kernel-type random projections of the raw image data with minimal power consumption and low latency. A back-end neural network can then reconstruct the original images with structural similarity exceeding 90%. This scheme has the potential to process data streams exceeding Terapixel/second using less than 100 fJ/pixel, providing a path to ultra-high-resolution data and image acquisition systems.

Date: 2024
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DOI: 10.1038/s41467-024-48099-2

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