AI 中文总结
提出一种硬件高效的混合触发器，结合轻量去噪器和紧凑分类器，在FPGA上实现高干扰环境下的稳健无线电触发，AUC达0.992，资源占用低且功耗亚瓦级。
AI 中文摘要
我们提出了一种用于基于FPGA的广延大气簇射无线电探测的硬件高效混合触发器。该混合设计由一个轻量级去噪器和一个紧凑分类器组成，去噪器用于清理原始ADC迹线，分类器则对去噪后的输出进行操作，从而在高干扰环境中实现稳健的近阈值脉冲检测。两个神经网络均采用量化感知训练。信号由探测器折叠的CoREAS/CORSIKA模拟生成，并嵌入实测噪声中以形成现实的基准测试。该触发器达到了0.992的AUC，同时轻松适配于Zynq-7000 Z-7020的资源预算内，具有微秒级延迟和亚瓦级功耗。RTL验证确认了定点硬件与量化软件模型之间的一致性，证明神经去噪与分类相结合可在嘈杂环境中提供可靠、低成本的无线电触发。
英文摘要
We present a hardware-efficient hybrid trigger for FPGA-based radio detection of extensive air showers. The hybrid design consists of a lightweight denoiser that cleans raw ADC traces and a compact classifier that operates on the denoised output, enabling robust near-threshold pulse detection in high-interference environments. Both neural networks are trained quantization-aware. Signals are generated from detector-folded CoREAS/CORSIKA simulations and embedded into measured noise to form a realistic benchmark. The trigger reaches an AUC of 0.992 while fitting comfortably within the resource budget of a Zynq-7000 Z-7020, with microsecond-scale latency and sub-watt power consumption. RTL validation confirms agreement between the fixed-point hardware and the quantized software model, demonstrating that neural denoising combined with classification provides reliable, low-cost radio triggering in noisy environments.
CommentsProceedings article for ARENA2026
