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arXiv:2506.08911 · v2

Implementing Keyword Spotting on the MCXN947 Microcontroller with Integrated NPU

Petar JakušHrvoje Džapo
cs.HCcs.ARcs.LGcs.PFcs.SD
首次提交 2025-06-10 23:38
版本更新 2026-11-05 20:38
雷达观测 2026-11-06 12:09
arXiv ↗PDF ↗

摘要

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.

作者备注：4 pages; v2: corrected mcu name in title

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v2 · 2026-11-05 20:38 · 最新观测版本

Implementing Keyword Spotting on the MCXN947 Microcontroller with Integrated NPU

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.

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