Implementing Keyword Spotting on the MCUX947 Microcontroller with Integrated NPU
Petar Jakuš, Hrvoje Džapo
Abstract
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.
Related Papers
Efficient Deployment of Spiking Neural Networks on SpiNNaker2 for DVS Gesture Recognition Using Neuromorphic Intermediate Representation2025-09-04An End-to-End DNN Inference Framework for the SpiNNaker2 Neuromorphic MPSoC2025-07-18Task-Specific Audio Coding for Machines: Machine-Learned Latent Features Are Codes for That Machine2025-07-17Angle Estimation of a Single Source with Massive Uniform Circular Arrays2025-07-17Quantized Rank Reduction: A Communications-Efficient Federated Learning Scheme for Network-Critical Applications2025-07-15MGVQ: Could VQ-VAE Beat VAE? A Generalizable Tokenizer with Multi-group Quantization2025-07-14Lightweight Federated Learning over Wireless Edge Networks2025-07-13Vision Foundation Models as Effective Visual Tokenizers for Autoregressive Image Generation2025-07-11