Optical information processing and computing can potentially offer enhanced performance, scalability and energy efficiency. However, achieving nonlinearity-a critical component of computation-remains challenging in the optical domain. Here we introduce a design that leverages a multiple-scattering cavity to passively induce optical nonlinear random mapping with a continuous-wave laser at a low power. Each scattering event effectively mixes information from different areas of a spatial light modulator, resulting in a highly nonlinear mapping between the input data and output pattern. We demonstrate that our design retains vital information even when the readout dimensionality is reduced, thereby enabling optical data compression. This capability allows our optical platforms to offer efficient optical information processing solutions across applications. We demonstrate our design's efficacy across tasks, including classification, image reconstruction, keypoint detection and object detection, all of which are achieved through optical data compression combined with a digital decoder. In particular, high performance at extreme compression ratios is observed in real-time pedestrian detection. Our findings open pathways for novel algorithms and unconventional architectural designs for optical computing.

翻译：光学信息处理与计算在性能、可扩展性和能效方面具有潜在优势。然而，在光学领域实现非线性——计算的关键组成部分——仍然具有挑战性。本文提出一种利用多重散射腔的设计，通过低功率连续波激光被动诱导光学非线性随机映射。每次散射事件有效混合了空间光调制器不同区域的信息，从而在输入数据与输出模式之间形成高度非线性映射。我们证明，即使在降低读出维度的情况下，该设计仍能保留关键信息，从而实现光学数据压缩。这一能力使我们的光学平台能够为各类应用提供高效的光学信息处理解决方案。我们在多项任务中验证了该设计的有效性，包括分类、图像重建、关键点检测和目标检测，所有这些任务均通过光学数据压缩结合数字解码器实现。特别是在实时行人检测中，即使在极端压缩比下仍观察到高性能表现。我们的研究结果为光学计算的新型算法和非传统架构设计开辟了道路。