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

Author

Listed:
  • 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
    Sandia National Laboratories)

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.

Suggested Citation

  • Xiao Wang & Brandon Redding & Nicholas Karl & Christopher Long & Zheyuan Zhu & James Skowronek & Shuo Pang & David Brady & Raktim Sarma, 2024. "Integrated photonic encoder for low power and high-speed image processing," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-48099-2
    DOI: 10.1038/s41467-024-48099-2
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    References listed on IDEAS

    as
    1. Yitong Chen & Maimaiti Nazhamaiti & Han Xu & Yao Meng & Tiankuang Zhou & Guangpu Li & Jingtao Fan & Qi Wei & Jiamin Wu & Fei Qiao & Lu Fang & Qionghai Dai, 2023. "All-analog photoelectronic chip for high-speed vision tasks," Nature, Nature, vol. 623(7985), pages 48-57, November.
    2. Daryl T. Spencer & Tara Drake & Travis C. Briles & Jordan Stone & Laura C. Sinclair & Connor Fredrick & Qing Li & Daron Westly & B. Robert Ilic & Aaron Bluestone & Nicolas Volet & Tin Komljenovic & Li, 2018. "An optical-frequency synthesizer using integrated photonics," Nature, Nature, vol. 557(7703), pages 81-85, May.
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