In-Sensor Radio Frequency Computing for Energy-Efficient Intelligent
  Radar
Yang Sui Minning Zhu Lingyi Huang Chung-Tse Michael Wu Bo Yuan
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.