arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2201.03787physics.opticscs.ET
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全光超快ReLU功能,用于节能纳米光子深度学习

All-optical ultrafast ReLU function for energy-efficient nanophotonic deep learning

Gordon H. Y. Li, Ryoto Sekine, Rajveer Nehra, Robert M. Gray, Luis Ledezma, Qiushi Guo, Alireza Marandi

摘要:

近年来,深度学习应用的计算需求使得引入节能硬件加速器成为必要。光学神经网络是一个很有前途的选择;然而,到目前为止,它们在很大程度上受到缺乏节能的非线性光学功能的限制。在这里,我们通过实验证明了全光学整流线性单元(ReLU),这是深度学习中使用最广泛的非线性激活函数,使用双极化薄膜锂酸盐纳米光子波导,并在每次激活的毫微微焦耳范围内实现超低能量,具有近瞬时操作。我们的研究结果为真正的全光学,节能纳米光子深度学习提供了一条清晰而实用的道路。

英文摘要:

In recent years, the computational demands of deep learning applications have necessitated the introduction of energy-efficient hardware accelerators. Optical neural networks are a promising option; however, thus far they have been largely limited by the lack of energy-efficient nonlinear optical functions. Here, we experimentally demonstrate an all-optical Rectified Linear Unit (ReLU), which is the most widely used nonlinear activation function for deep learning, using a periodically-poled thin-film lithium niobate nanophotonic waveguide and achieve ultra-low energies in the regime of femtojoules per activation with near-instantaneous operation. Our results provide a clear and practical path towards truly all-optical, energy-efficient nanophotonic deep learning.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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