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高计算密度纳米光子介质用于机器学习推断.

Zhenyu Zhao1, Yichen Pan1, Jinlong Xiang1

  • 1State Key Laboratory of Photonics and Communications, School of Information Science and Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China.

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|November 21, 2025
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概括

研究人员使用纳米光子介质开发了超紧的光学神经网络,用于高效的机器学习推断. 这一突破使密集,低功耗的人工智能 (AI) 处理器用于下一代应用程序.

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科学领域:

  • 光子学 是一个光子学.
  • 人工智能的人工智能
  • 材料科学 材料科学 材料科学

背景情况:

  • 有效的机器学习推断对于人工智能采用至关重要.
  • 芯片上的光学计算提供了低功耗,但面临着小型化挑战.
  • 传统的光学神经网络缺乏计算密度.

研究的目的:

  • 在纳米光子介质中展示制造受限散射光学计算.
  • 为了实现超紧的光学神经架构.
  • 为了克服光学计算中的小型化局限性.

主要方法:

  • 用于纳米光子介质的制造意识反向设计.
  • 在这些介质中开发了散射光学计算.
  • 制造的超紧的光学神经架构 (64μm2).

主要成果:

  • 在原型的虹膜数据集上实现了86.7%的准确性.
  • 通过8x8手写数字光学字符识别设计证明了可扩展性.
  • 在光学字符识别方面达到92.8%的测试准确度.

结论:

  • 纳米光子介质使得在极小的足迹中实现大规模的人工智能任务.
  • 开发的方法为密集,节能的光学处理器铺平了道路.
  • 这项研究为下一代人工智能推进光学计算.