使用数字双胞胎使用多模光学非线性训练混合神经网络
Ilker Oguz1, Louis J E Suter1, Jih-Liang Hsieh1
1EPFL, Institute of Electrical and Micro Engineering, 1015 Lausanne, Switzerland.
Nanophotonics (Berlin, Germany)
|August 13, 2025
概括
这项研究将物理系统集成到人工智能 (AI) 模型中,使用多模纤维中的超短脉冲传播来创建节能AI. 这种方法显著减少了训练大型神经网络的计算需求.
科学领域:
- 人工智能的人工智能
- 光学物理学 光学物理学
- 计算科学 计算科学
背景情况:
- 神经网络的规模不断增加,需要大量的能量和计算资源.
- 复杂的物理事件可以作为高效的计算模块集成,以减少可训练层的复杂性.
研究的目的:
- 开发一种混合人工智能架构,利用物理系统进行高效的计算.
- 为了减少训练大型神经网络的能量和计算需求.
主要方法:
- 在多模纤维中利用超短脉冲传播进行大规模非线性转换.
- 开发了一种神经模型,以差分接近光学系统进行训练.
- 采用一个训练算法,通过光学代理反向传播错误信号.
主要成果:
- 实现了最先进的图像分类准确度.
- 证明了高仿真真实性和异常的耐试验漂移.
- 成功地将低能耗物理系统集成到神经网络架构中.
结论:
- 这种混合方法可以实现可扩展和节能的人工智能模型.
- 通过整合物理计算模块,降低了人工智能培训的计算需求.
- 提供了一条通往更可持续和更强大的人工智能的道路.
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