Comparison of crosstalk prediction accuracy.

Electromagnetic interference (EMI) analysis in high-speed industrial systems is increasingly challenged by multi-gigahertz sampling rates, complex transient behaviors, and stringent real-time constraints. To address these challenges, this paper proposes a pulse-aware generative and analysis framework based on a generative adversarial network (GAN) combined with pulse sparse convolution using leaky integrate-and-fire (LIF) spiking neurons. A multi-scale discriminator and gradient penalty stabilization are employed to improve waveform generation fidelity, achieving a Fréchet distance (FID) of 0.72 and a global difference metric (GDM) of 0.18 ± 0.03 on an industrial-grade Electromagnetic compatibility (EMC) dataset. The proposed framework is further applied to crosstalk prediction, where it reduces pulse-width and phase prediction errors by more than 40% compared with classical numerical solvers such as finite-difference time-domain (FDTD), finite element method (FEM), and method of moments (MoM), and consistently outperforms representative learning-based EMC models. To enable real-time deployment, the pulse sparse convolution architecture is implemented on an field-programmable gate array (FPGA) platform using fixed-point arithmetic, achieving deterministic inference at 5 GS/s with a measured power consumption of 0.71 W. Extensive experiments on traction systems, industrial robots, CNC drives, photovoltaic inverters, and UAV (Unmanned Aerial Vehicle) electronics demonstrate that the proposed approach provides accurate, stable, and energy-efficient EMI analysis suitable for practical industrial EMC applications.

高速工业系统中的电磁干扰（EMI）分析正面临愈发严峻的挑战：多吉赫兹采样速率、复杂瞬态特性以及严苛的实时性约束。为应对这些难题，本文提出一种基于生成对抗网络（GAN）的脉冲感知生成与分析框架，结合了采用漏积分放电（LIF）脉冲神经元的脉冲稀疏卷积。该框架采用多尺度判别器与梯度惩罚稳定机制以提升波形生成保真度，在工业级电磁兼容（EMC）数据集上实现了0.72的弗雷歇距离（FID）与0.18±0.03的全局差异度量（GDM）。所提框架进一步应用于串扰预测任务，相较于时域有限差分法（FDTD）、有限元法（FEM）与矩量法（MoM）等经典数值求解器，其将脉宽与相位预测误差降低了40%以上，且始终优于代表性的基于学习的EMC模型。为实现实时部署，该脉冲稀疏卷积架构采用定点算术在现场可编程门阵列（FPGA）平台上实现，可在5吉采样每秒（GS/s）的速率下完成确定性推理，实测功耗为0.71瓦。在牵引系统、工业机器人、数控驱动器、光伏逆变器与无人机（UAV）电子设备上开展的大量实验验证表明，所提方法可提供精准、稳定且低功耗的EMI分析方案，适用于实际工业EMC应用场景。