Ultrafast microwave sensing and automatic recognition of dynamic objects in open world using programmable surface plasmonic neural networks
🤖 AI Summary
This work proposes a programmable surface plasmon polariton neural network (P-SPNN) to overcome the limitations of existing microwave sensing systems, which suffer from low refresh rates (only a few hundred hertz) and limited programmability due to serial digital processing, hindering high-speed adaptive perception of dynamic targets in open environments. By integrating a programmable neural network architecture with surface plasmon polariton technology, P-SPNN enables fully analog signal processing directly in the microwave domain, establishing an all-analog signal pathway. The system incorporates 288 programmable phase-modulating neurons with inherent beam-scanning capability, substantially surpassing conventional systems in speed and energy efficiency. In real-world road scenarios, it achieves 91%–97% accuracy in pedestrian and vehicle recognition, with a perception latency of merely 25 nanoseconds, a refresh rate exceeding 10 kHz, and an energy efficiency of 17 TOPS/W.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
microwave sensing
dynamic object recognition
real-time perception
open-world deployment
programmable hardware
Innovation
Methods, ideas, or system contributions that make the work stand out.
programmable surface plasmonic neural network
ultrafast microwave sensing
real-time object recognition
analog neuromorphic hardware
open-world perception
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