# End-to-End Efficiency in Keyword Spotting: A System-Level Approach for Embedded Microcontrollers

arXiv: https://arxiv.org/abs/2509.07051 · HTML: https://mlsanity.com/paper/2509.07051/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 21 references that are in the map.
- First posted on arXiv 8 Sep 2025.

## Builds on (most cited first, 15 of 15)

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [Hello Edge: Keyword Spotting on Microcontrollers](https://mlsanity.com/paper/1711.07128/) (2017)
- [Deep Spoken Keyword Spotting: An Overview](https://mlsanity.com/paper/2111.10592/) (2021)
- [Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting](https://mlsanity.com/paper/1703.05390/) (2017)
- [Small-Footprint Keyword Spotting on Raw Audio Data with Sinc-Convolutions](https://mlsanity.com/paper/1911.02086/) (2019)
- [P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applications](https://mlsanity.com/paper/2203.04737/) (2022)
- [Small-Footprint Keyword Spotting with Multi-Scale Temporal Convolution](https://mlsanity.com/paper/2010.09960/) (2020)
- [On-Device Domain Learning for Keyword Spotting on Low-Power Extreme Edge Embedded Systems](https://mlsanity.com/paper/2403.10549/) (2024)
- [Hardware Aware Training for Efficient Keyword Spotting on General Purpose and Specialized Hardware](https://mlsanity.com/paper/2009.04465/) (2020)
- [Ultra-Low Power Keyword Spotting at the Edge](https://mlsanity.com/paper/2111.04988/) (2021)
- [Keyword Spotting System and Evaluation of Pruning and Quantization Methods on Low-power Edge Microcontrollers](https://mlsanity.com/paper/2208.02765/) (2022)
- [LiCo-Net: Linearized Convolution Network for Hardware-efficient Keyword Spotting](https://mlsanity.com/paper/2211.04635/) (2022)
- [Benchmarking Energy and Latency in TinyML: A Novel Method for Resource-Constrained AI](https://mlsanity.com/paper/2505.15622/) (2025)
- [Adaptive Noise Resilient Keyword Spotting Using One-Shot Learning](https://mlsanity.com/paper/2505.09304/) (2025)

## Built on by (most cited first, 3 of 3)

- [Performance Analysis of Edge and In-Sensor AI Processors: A Comparative Review](https://mlsanity.com/paper/2603.08725/) (2026)
- [CREST: Deployment-Realistic Hardware-in-the-Loop NAS for Embedded Sensing Systems](https://mlsanity.com/paper/2606.15004/) (2026)
- [Sub-Model Short-Term Memory Convolutions for Keyword Spotting Systems on Device](https://mlsanity.com/paper/2609.35005/) (2026)

## Abstract (arXiv)

Keyword spotting (KWS) is a key enabling technology for hands-free interaction in embedded and IoT devices, where stringent memory and energy constraints challenge the deployment of AI-enabeld devices. In this work, we systematically evaluate and compare several state-of-the-art lightweight neural network architectures, including DS-CNN, LiCoNet, and TENet, alongside our proposed Typman-KWS (TKWS) architecture built upon MobileNet, specifically designed for efficient KWS on microcontroller units (MCUs). Unlike prior studies focused solely on model inference, our analysis encompasses the entire processing pipeline, from Mel-Frequency Cepstral Coefficient (MFCC) feature extraction to neural inference, and is benchmarked across three STM32 platforms (N6, H7, and U5). Our results show that TKWS with three residual blocks achieves up to 92.4% F1-score with only 14.4k parameters, reducing memory footprint without compromising the accuracy. Moreover, the N6 MCU with integrated neural acceleration achieves the best energy-delay product (EDP), enabling efficient, low-latency operation even with high-resolution features. Our findings highlight the model accuracy alone does not determine real-world effectiveness; rather, optimal keyword spotting deployments require careful consideration of feature extraction parameters and hardware-specific optimization.