A FPGA-based Energy-Efficient Processor for Radar-based Continuous Fall Detection

This paper proposes a FPGA-based energy-efficient processor for radar-based continuous fall detection, consisting of a spectrogram generation circuit and a convolutional neural network (CNN). To reduce resource consumption and power usage of the entire processor, two designs were implemented: 1) A serial-FFT-based spectrogram generation circuit for radar signal preprocessing, and 2) An neural network (NN) accelerator based on row-stationary dataflow has been designed. By using the updated block wise computation technique, the accelerator enables the computation of only the NN’s updated inputs, resulting in an 85% reduction in multiply-accumulate (MAC) operations and a 79% reduction in intermediate result storage. Implemented on the Xilinx FPGA board ZC702, this processor consumes only 7.3k LUTs, 3.6k Flip Flops (FFs), 22 Block RAMs (BRAMs), and 10 DSPs, with a power consumption of only 0.302W. On an open-source radar fall detection dataset, this processor achieves an accuracy of 99.58%. The processor incurs a delay of only 0.431ms for a single preprocessing and NN inference, consuming just 130.16 µJ.