Photonic edge intelligence chip for multi-modal sensing, inference and learning

Shiji Zhang, Xueyi Jiang, Bo Wu, Haojun Zhou, Wenguang Xu, Hailong Zhou, Zhichao Ruan, Jianji Dong () and Xinliang Zhang
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Shiji Zhang: Huazhong University of Science and Technology, Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information
Xueyi Jiang: Huazhong University of Science and Technology, Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information
Bo Wu: Huazhong University of Science and Technology, Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information
Haojun Zhou: Huazhong University of Science and Technology, Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information
Wenguang Xu: Huazhong University of Science and Technology, Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information
Hailong Zhou: Huazhong University of Science and Technology, Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information
Zhichao Ruan: Zhejiang University, State Key Laboratory of Extreme Photonics and Instrumentation, School of Physics, and College of Optical Science and Engineering
Jianji Dong: Huazhong University of Science and Technology, Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information
Xinliang Zhang: Huazhong University of Science and Technology, Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract Edge computing requires real-time processing of high-throughput analog signals, posing a major challenge to conventional electronics. Although integrated photonics offers low-latency processing, it struggles to directly handle raw analog data. Here, we present a photonic edge intelligence chip (PEIC) that fuses multiple analog modalities—images, spectra, and radio-frequency signals—into broad optical spectra for single-fiber input. After transmission onto the chip, these spectral inputs are processed by an arrayed waveguide grating (AWG) that performs both spectral sensing and energy-efficient convolution (29 fJ/OP). A subsequent nonlinear activation layer and a fully connected layer form an end-to-end optical neural network, achieving on-chip inference with a measured response time of 1.33 ns. We demonstrate both supervised and unsupervised learning on three tasks: drug spectral recognition, image classification, and radar target classification. Our work paves the way for on-chip solutions that unify analog signal acquisition and optical computation for edge intelligence.

Date: 2025
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DOI: 10.1038/s41467-025-65151-x

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