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, Xinliang Zhang
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. Edge devices require real-time processing of high-throughput analog signals. Here, authors present a photonic intelligence chip that fuses multiple analog signal types into optical spectra for ultra-fast, energy-efficient on-chip AI computation, enabling diverse edge intelligence applications.