Deep learning has fundamentally transformed artificial intelligence, but the ever-increasing complexity in deep learning models calls for specialized hardware accelerators. Optical accelerators can potentially offer enhanced performance, scalability, and energy efficiency. However, achieving nonlinear mapping, a critical component of neural networks, remains challenging optically. Here, we introduce a design that leverages multiple scattering in a reverberating cavity to passively induce optical nonlinear random mapping, without the need for additional laser power. A key advantage emerging from our work is that we show we can perform optical data compression, facilitated by multiple scattering in the cavity, to efficiently compress and retain vital information while also decreasing data dimensionality. This allows rapid optical information processing and generation of low dimensional mixtures of highly nonlinear features. These are particularly useful for applications demanding high-speed analysis and responses such as in edge computing devices. Utilizing rapid optical information processing capabilities, our optical platforms could potentially offer more efficient and real-time processing solutions for a broad range of applications. We demonstrate the efficacy of our design in improving computational performance across tasks, including classification, image reconstruction, key-point detection, and object detection, all achieved through optical data compression combined with a digital decoder. Notably, we observed high performance, at an extreme compression ratio, for real-time pedestrian detection. Our findings pave the way for novel algorithms and architectural designs for optical computing.

翻译：深度学习从根本上改变了人工智能，但深度学习模型日益增长的复杂性需要专用硬件加速器。光学加速器具有提升性能、可扩展性和能效的潜力。然而，实现神经网络的关键组件——非线性映射，在光学领域仍具挑战性。本文提出一种设计，利用回音腔中多次散射被动诱导光学非线性随机映射，无需额外激光功率。我们工作的一个关键优势在于，通过腔体中的多次散射实现光学数据压缩，有效压缩并保留关键信息，同时降低数据维度。这实现了快速光学信息处理，并生成低维混合的高度非线性特征。这些特性对于边缘计算设备等需要高速分析与响应的应用尤其有用。利用快速光学信息处理能力，我们的光学平台有望为广泛应用提供更高效、实时的处理方案。我们在分类、图像重建、关键点检测及目标检测等任务中验证了该设计对计算性能的提升效果，所有任务均通过光学数据压缩与数字解码器结合实现。值得注意的是，在极端压缩比下，我们观察到实时行人检测的高性能表现。我们的研究为光学计算的新型算法与架构设计铺平了道路。