A Low-Cost Energy-Efficient FFT-CNN Processor for Versatile mmWave Radar in IoT Applications

The millimeter-wave (mmWave) radar Internet of Things (IoT) applications based on Convolutional Neural Network (CNN) algorithms are widely employed across various industries. However, they face challenges in deployment on low-cost and resource-constrained edge devices. Current CNN accelerators can efficiently handle CNN computations but cannot effectively accelerate the essential fast Fourier Transform (FFT) computations in mmWave radar signal processing, thus failing to achieve end-to-end high-efficiency radar signal processing. This work presents a low-cost energy-efficient FFT-CNN processor with a unified architecture that combines CNN and FFT to enhance performance for mmWave radar IoT applications. The proposed FFT-CNN processor employs a reconfigurable dual-mode processing element to maximize hardware resource sharing, supporting both butterfly operations for FFT and multiply-accumulate computations for CNN acceleration. This sharing technique reduces hardware resource consumption by 22.1% in LUTs, 22.7% in FFs, 9.5% in BRAMs, and 16.7% in DSPs, leading to a total power reduction of 34.9%. Compared to state-of-the-art processors optimized for radar applications, the proposed processor achieves the highest performance-to-resource ratio and energy efficiency, making it ideal for a wide range of low-cost radar IoT applications.