FPGA- SoC
Network Interface Cards (NICs) are one of the key enablers of the modern-day Internet. They serve as gateways for connecting computing devices to networks for the exchange of data with other devices. Recently, the pervasive nature of Internet-enabled devices coupled with the growing demands for faster network access have necessitated the enhancement of NICs to Smart NICs (SNICs), capable of processing enormous volumes of data at near real-time speed.
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This paper proposes a novel Recursive Convolutional Target Detector (RCTD) for Frequency-Modulated Continuous-Wave (FMCW) radar in complex automotive scenarios. Leveraging a lightweight convolutional neural network, RCTD efficiently localizes multiple targets despite strong interference. Detailed simulations and a hardware prototype on an FPGA-based deep learning processor demonstrate real-time feasibility, low false alarm rates, and higher detection accuracy under stringent resource constraints.
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