In-Sensor Radio Frequency Computing for Energy-Efficient Intelligent Radar
This work addresses energy efficiency and hardware scalability for intelligent radar systems, though it appears incremental as it builds on existing RFNN methods with optimizations.
The paper tackles the high energy and hardware cost of large-scale Radio Frequency Neural Networks (RFNNs) by proposing a low-rank tensor-train decomposition method (TT-RFNN) and a robust variant (RTT-RFNN), achieving notable parameter reduction and energy efficiency while preserving accuracy on datasets like MNIST and CIFAR-10.
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.