论文标题
用于节能的纳米光学深度学习的全光超快恢复功能
All-optical ultrafast ReLU function for energy-efficient nanophotonic deep learning
论文作者
论文摘要
近年来，深度学习应用的计算需求需要引入节能硬件加速器。光学神经网络是一个有前途的选择；但是，到目前为止，它们在很大程度上受到缺乏节能非线性光学功能的限制。在这里，我们在实验上展示了一个全光矫正的线性单元（RELU），该单元是深度学习的最广泛使用的非线性激活函数，使用定期粘贴的薄膜尼古德纳米型含量波导，并实现aiviemation per Acipime per Acipime per Interstantantantantantantantantantantantantanteantantantantantantantantantantantanteantanteantanteantaine secime for Femotojoules perimime。我们的结果为真正的全光能，节能的纳米光学深度学习提供了清晰而实用的途径。
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.
