Neural-HAR: A Dimension-Gated CNN Accelerator for Real-Time Radar Human Activity Recognition

• URL: http://arxiv.org/abs/2510.22772v1
• Date: Sun, 26 Oct 2025 17:42:28 GMT
• Title: Neural-HAR: A Dimension-Gated CNN Accelerator for Real-Time Radar Human Activity Recognition
• Authors: Yizhuo Wu, Francesco Fioranelli, Chang Gao,
• Abstract summary: We introduce a dimension-gated CNN accelerator tailored for real-time radar HAR on resource-constrained platforms.<n>GateCNN attains 86.4% accuracy with only 2.7k parameters and 0.28M FLOPs per inference, comparable to CNN-BiGRU at a fraction of the complexity.<n>Our FPGA prototype on Xilinx Zynq-7000 Z-7007S reaches 107.5 $mu$s latency and 15 mW dynamic power using LUT-based ROM and distributed RAM only.
• Score: 5.400353553418959
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Radar-based human activity recognition (HAR) is attractive for unobtrusive and privacy-preserving monitoring, yet many CNN/RNN solutions remain too heavy for edge deployment, and even lightweight ViT/SSM variants often exceed practical compute and memory budgets. We introduce Neural-HAR, a dimension-gated CNN accelerator tailored for real-time radar HAR on resource-constrained platforms. At its core is GateCNN, a parameter-efficient Doppler-temporal network that (i) embeds Doppler vectors to emphasize frequency evolution over time and (ii) applies dual-path gated convolutions that modulate Doppler-aware content features with temporal gates, complemented by a residual path for stable training. On the University of Glasgow UoG2020 continuous radar dataset, GateCNN attains 86.4% accuracy with only 2.7k parameters and 0.28M FLOPs per inference, comparable to CNN-BiGRU at a fraction of the complexity. Our FPGA prototype on Xilinx Zynq-7000 Z-7007S reaches 107.5 $\mu$s latency and 15 mW dynamic power using LUT-based ROM and distributed RAM only (zero DSP/BRAM), demonstrating real-time, energy-efficient edge inference. Code and HLS conversion scripts are available at https://github.com/lab-emi/AIRHAR.

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