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English(EN) Photonic convolutional neural network with pre-trained in situ training

光子CNN实现高精度和高能效

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种全光子卷积神经网络（PCNN），能够完全在光学域内执行图像分类任务。这种新颖的架构使用马赫-曾德尔干涉仪网格和微环谐振器等光学元件集成了卷积、最大池化、非线性激活和全连接层。虽然在MNIST数据集上达到了94.49%的准确率，但经过异地预训练的数字孪生达到了97.45%的准确率。PCNN在能效方面显示出巨大潜力，对于单图像推理，其能效估计比最先进的电子GPU高220到330倍，同时推理延迟低至843纳秒。 AI

影响 为实现显著更节能、更快速的AI推理硬件提供了途径。

排序理由 详细介绍AI新型硬件架构的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

光子CNN实现高精度和高能效

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详细介绍AI新型硬件架构的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Saurabh Ranjan, Sonika Thakral, Amit Sehgal ·

    具有预训练原位训练的光子卷积神经网络

    arXiv:2604.02429v2 Announce Type: replace-cross Abstract: Convolutional neural networks (CNNs) have transformed image processing, but the energy consumption and inference latency of electronic based implementations remain fundamental bottlenecks. These limitations have motivated …

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