Radio Frequency Neural Networks (RFNNs) have demonstrated advantages in realizing intelligent applications across various domains. However, as the model size of deep neural networks rapidly increases, implementing large-scale RFNN in practice requires an extensive number of RF interferometers and consumes a substantial amount of energy. To address this challenge, we propose to utilize low-rank decomposition to transform a large-scale RFNN into a compact RFNN while almost preserving its accuracy. Specifically, we develop a Tensor-Train RFNN (TT-RFNN) where each layer comprises a sequence of low-rank third-order tensors, leading to a notable reduction in parameter count, thereby optimizing RF interferometer utilization in comparison to the original large-scale RFNN. Additionally, considering the inherent physical errors when mapping TT-RFNN to RF device parameters in real-world deployment, from a general perspective, we construct the Robust TT-RFNN (RTT-RFNN) by incorporating a robustness solver on TT-RFNN to enhance its robustness. To adapt the RTT-RFNN to varying requirements of reshaping operations, we further provide a reconfigurable reshaping solution employing RF switch matrices. Empirical evaluations conducted on MNIST and CIFAR-10 datasets show the effectiveness of our proposed method.

翻译：射频神经网络（RFNN）已在多个领域展现出实现智能化应用的优势。然而，随着深度神经网络模型规模的快速增长，实际部署大规模RFNN需要大量射频干涉仪并消耗巨大能量。针对这一挑战，我们提出利用低秩分解将大规模RFNN压缩为紧凑型RFNN，同时几乎保持其原有精度。具体而言，我们开发了张量列射频神经网络（TT-RFNN），其中每一层由一系列低秩三阶张量构成，与原始大规模RFNN相比，参数数量显著减少，从而优化了射频干涉仪的利用率。此外，考虑到实际部署中将TT-RFNN映射到射频设备参数时存在的固有无理误差，我们从一般性角度出发，通过在TT-RFNN中引入鲁棒性求解器构建鲁棒张量列射频神经网络（RTT-RFNN），以增强其鲁棒性。为适应RTT-RFNN对重塑操作的不同需求，我们进一步提出一种采用射频开关矩阵的可重构重塑方案。在MNIST和CIFAR-10数据集上的实验结果表明了所提方法的有效性。