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Showing 1–9 of 9 results
Advanced filters: Author: Tiankuang Zhou Clear advanced filters

  • The study presents a single-layer photonic computing (SLiM) chip that mitigates error accumulation by decoupling redundancies. It supports deep learning over 200 layers and enables large language models, advancing efficient photonic hardware for AI.

    • Tiankuang Zhou
    • Yizhou Jiang
    • Lu Fang
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-13

  • This study reports a complete photonic neuron integrated on a silicon-nitride chip, enabling ultrafast all-optical computing with nonlinear multi-kernel convolution for image recognition and motion generation.

    • Tao Yan
    • Yanchen Guo
    • Lu Fang
    Research
    Nature Computational Science
    Volume: 5, P: 1202-1213

  • We present fully forward mode learning, which conducts machine learning operations on site, leading to faster learning and promoting advancement in numerous fields.

    • Zhiwei Xue
    • Tiankuang Zhou
    • Lu Fang
    ResearchOpen Access
    Nature
    Volume: 632, P: 280-286

  • Optoelectronic neural networks are a promising avenue in AI computing for parallelization, power efficiency, and speed. Here, the authors present a dual-neuron optical-artificial learning approach for training large-scale diffractive neural networks, achieving VGG-level performance on ImageNet in simulation with a network that is 10 times larger than existing ones.

    • Xiaoyun Yuan
    • Yong Wang
    • Lu Fang
    ResearchOpen Access
    Nature Communications
    Volume: 14, P: 1-10

  • An all-analog chip combining electronic and light computing achieves systemic energy efficiency of more than three orders of magnitude and a computing speed of more than one order of magnitude compared with state-of-the-art computing processors.

    • Yitong Chen
    • Maimaiti Nazhamaiti
    • Qionghai Dai
    ResearchOpen Access
    Nature
    Volume: 623, P: 48-57

  • Inspired by human brain for multi-task continual learning, a generalized photonic neuromorphic architecture (L2ONN) is proposed to model physical-driven light sparsity and parallelism, towards reconfigurable and scalable lifelong learning.

    • Yuan Cheng
    • Jianing Zhang
    • Lu Fang
    ResearchOpen Access
    Light: Science & Applications
    Volume: 13, P: 1-12

  • We develop Monet: a multichannel optical neural network architecture for a universal multiple-input multiple-channel optical computing based on a novel projection-interference-prediction framework, solving real-world advanced machine vision tasks optically.

    • Zhihao Xu
    • Xiaoyun Yuan
    • Lu Fang
    ResearchOpen Access
    Light: Science & Applications
    Volume: 11, P: 1-13