In-Sensor Radio Frequency Computing for Energy-Efficient Intelligent Radar

• URL: http://arxiv.org/abs/2312.10343v1
• Date: Sat, 16 Dec 2023 06:21:42 GMT
• Title: In-Sensor Radio Frequency Computing for Energy-Efficient Intelligent Radar
• Authors: Yang Sui, Minning Zhu, Lingyi Huang, Chung-Tse Michael Wu, Bo Yuan
• Abstract summary: We propose to transform a large-scale RFNN into a compact RFNN while almost preserving its accuracy. We construct the Robust TT-RFNN (RTT-RFNN) by incorporating a robustness solver on TT-RFNN to enhance its robustness. Empirical evaluations conducted on MNIST and CIFAR-10 datasets show the effectiveness of our proposed method.
• Score: 8.041399176135178
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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