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

arXiv: https://arxiv.org/abs/2510.22772 · HTML: https://mlsanity.com/paper/2510.22772/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 26 Oct 2025.

## Builds on (most cited first, 5 of 5)

- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Fast inference of deep neural networks in FPGAs for particle physics](https://mlsanity.com/paper/1804.06913/) (2018)
- [Survey on Hand Gesture Recognition from Visual Input](https://mlsanity.com/paper/2501.11992/) (2025)
- [Prior-Guided Deep Interference Mitigation for FMCW Radars](https://mlsanity.com/paper/2108.13023/) (2021)
- [ASL Trigger Recognition in Mixed Activity/Signing Sequences for RF Sensor-Based User Interfaces](https://mlsanity.com/paper/2111.05480/) (2021)

## Abstract (arXiv)

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 μs 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.