This paper presents a keyword spotting (KWS) system implemented on the NXP MCXN947 microcontroller with an integrated Neural Processing Unit (NPU), enabling real-time voice interaction on resource-constrained devices. The system combines MFCC feature extraction with a CNN classifier, optimized using Quantization Aware Training to reduce model size with minimal accuracy drop. Experimental results demonstrate a 59x speedup in inference time when leveraging the NPU compared to CPU-only execution, achieving 97.06% accuracy with a model size of 30.58 KB, demonstrating the feasibility of efficient, low-power voice interfaces on embedded platforms.
View on arXiv@article{jakuš2025_2506.08911,
title={ Implementing Keyword Spotting on the MCUX947 Microcontroller with Integrated NPU },
author={ Petar Jakuš and Hrvoje Džapo },
journal={arXiv preprint arXiv:2506.08911},
year={ 2025 }
}