Deep neural networks have achieved remarkable breakthroughs by leveraging multiple layers of data processing to extract hidden representations, albeit at the cost of large electronic computing power. To enhance energy efficiency and speed, the optical implementation of neural networks aims to harness the advantages of optical bandwidth and the energy efficiency of optical interconnections. In the absence of low-power optical nonlinearities, the challenge in the implementation of multilayer optical networks lies in realizing multiple optical layers without resorting to electronic components. In this study, we present a novel framework that uses multiple scattering that is capable of synthesizing programmable linear and nonlinear transformations concurrently at low optical power by leveraging the nonlinear relationship between the scattering potential, represented by data, and the scattered field. Theoretical and experimental investigations show that repeating the data by multiple scattering enables non-linear optical computing at low power continuous wave light. Moreover, we empirically found that scaling of this optical framework follows the power law as in state-of-the-art deep digital networks.

翻译：深度神经网络通过多层数据处理提取隐藏表征，实现了突破性进展，但这以巨大的电子计算能力为代价。为提升能效与速度，神经网络的光学实现旨在利用光带宽优势和光互连的能效特性。在缺乏低功耗光学非线性机制的情况下，构建多层光学网络的核心挑战在于如何在不依赖电子元件的前提下实现多层光信号处理。本研究提出一种新型框架，利用多重散射技术，通过建立由数据表征的散射势与散射场之间的非线性关系，能够在低光功率下同时实现可编程的线性与非线性变换。理论与实验研究表明，通过多重散射重复数据，可在连续低功率光波条件下实现非线性光学计算。此外，我们通过实验发现，该光学框架的扩展规律与前沿深度数字网络中的幂律规则一致。