The evolution toward next-generation intelligent sensing requires microwave systems to move beyond static detection and achieve high-speed and adaptive perception of dynamic scenes. However, the existing microwave sensing systems have bottlenecks owing to their sequential digital processing chain, limiting the refresh rates to hundreds of hertz, while the existing integrated microwave processors are lack of programmable and scalable capabilities for robust and open-world deployment. To break the bottlenecks, here we report a programmable surface plasmonic neural network (P-SPNN) that enables real-time microwave sensing and automatic recognition of dynamic objects in open-world environment. With a perception latency of 25 ns and a refresh rate exceeding 10 kHz, the P-SPNN system operates more than two orders of magnitude faster than the conventional millimeter-wave sensors, while achieving an energy efficiency of 17 TOPS per W. With 288 programmable phase-modulated neurons, we demonstrate real time and robust classification of persons and cars with 91-97% accuracy in the open road scenarios. By further integrating beam-scanning function, P-SPNN enables multi-dimensional spatial temporal frequency sensing without the digital preprocessing. These results establish P-SPNN as a programmable, scalable, and low-power platform for high-speed perception tasks in realistic world, with broad implications for autonomous driving, intelligent sensing, and next-generation artificial intelligence hardware.

翻译：向下一代智能感知演进要求微波系统突破静态检测，实现动态场景的高速自适应感知。然而现有微波感测系统受限于顺序数字处理链路，刷新率被限制在数百赫兹，同时现有集成微波处理器缺乏针对开放世界鲁棒部署所需的可编程与可扩展能力。为突破这些瓶颈，本文报道了一种可编程表面等离激元神经网络（P-SPNN），实现开放世界环境下动态物体的实时微波感测与自动识别。该P-SPNN系统感知延迟为25纳秒，刷新率超过10千赫兹，运行速度比传统毫米波传感器快两个数量级以上，同时能效达到17 TOPS/W。利用288个可编程相位调制神经元，我们展示了在开放道路场景下对行人与车辆91-97%精度的实时鲁棒分类。通过进一步集成波束扫描功能，P-SPNN无需数字预处理即可实现多维时空频率感知。这些成果确立了P-SPNN作为可编程、可扩展、低功耗平台在真实世界高速感知任务中的应用价值，对自动驾驶、智能感知及下一代人工智能硬件具有深远意义。