基于热原子非线性的自由空间光学神经网络
Free-space optical neural network based on thermal atomic nonlinearity
摘要：
随着人工神经网络（ANN）继续在广泛和多样化的技术领域中取得进展，对超越传统电子设备的更有效的硬件实现的搜索正在获得牵引力。特别是，由于自由空间光学器件的固有并行性，光学实现可能在速度和降低的能耗方面提供非凡的增益。同时，物理非线性，人工神经网络的一个关键组成部分，是不容易实现的自由空间光学，这限制了该平台的潜力。这个问题进一步恶化，因为需要对每个数据点并行执行非线性激活，以保持线性自由空间光学的益处。在这里，我们提出了一个自由空间光学人工神经网络与衍射为基础的线性加权求和和非线性激活热原子的饱和吸收启用。我们证明，通过模拟和实验，图像分类的手写数字只使用一个单一的层，并观察到6%的分类精度提高，由于光学非线性相比，线性模型。我们的平台即使在物理非线性的情况下也保留了自由空间光学的大量并行性，从而为光学ANN的新颖设计和更广泛的部署开辟了道路。
英文摘要：
As artificial neural networks (ANNs) continue to make strides in wide-ranging and diverse fields of technology, the search for more efficient hardware implementations beyond conventional electronics is gaining traction. In particular, optical implementations potentially offer extraordinary gains in terms of speed and reduced energy consumption due to intrinsic parallelism of free-space optics. At the same time, a physical nonlinearity, a crucial ingredient of an ANN, is not easy to realize in free-space optics, which restricts the potential of this platform. This problem is further exacerbated by the need to perform the nonlinear activation also in parallel for each data point to preserve the benefit of linear free-space optics. Here, we present a free-space optical ANN with diffraction-based linear weight summation and nonlinear activation enabled by the saturable absorption of thermal atoms. We demonstrate, via both simulation and experiment, image classification of handwritten digits using only a single layer and observed 6-percent improvement in classification accuracy due to the optical nonlinearity compared to a linear model. Our platform preserves the massive parallelism of free-space optics even with physical nonlinearity, and thus opens the way for novel designs and wider deployment of optical ANNs.
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。
