Low-Power Learnable Digital Audio Feature Extractor for Always-on Keyword Spotting in Edge Devices
Chen Shen, Jinhai Hu, Wang Ling Goh, Yi Sheng Chong, Anh Tuan Do, Yuan Gao · IEEE Transactions on Circuits and Systems I Regular Papers · 2025
This paper presents a low-power, learnable digital audio feature extractor (AuFEx) for always-on keyword spotting (KWS) in edge devices. A noise-aware design flow is introduced to integrate the tuning of AuFEx parameters directly into the neural network classifier’s training process. This co-design approach enables the joint optimization of feature extractor and classifier within a unified framework. By incorporating noise during the training process, the system becomes more robust to variations in signal-to-noise ratio (SNR), maintaining high inference accuracy even with lightweight neural network classifiers. This design flow supports the design of both time-domain AuFEx (TD-AuFEx) and frequency-domain AuFEx (FD-AuFEx) for single-keyword wake-word detection (WWD) and 10-keyword KWS tasks, respectively. Implemented in a 40nm CMOS process, both TD-AuFEx and FD-AuFEx achieve over 2% accuracy improvement with the smallest size backend classifier. Specifically, the TD-AuFEx for WWD task achieves classifier size of 3.1k parameters with 494 nW power consumption and$375~\mu $s latency. The accuracy is maintained between 96.2% – 97.9% for SNR in the range of 5 – 20 dB. The FD-AuFEx for 10-keyword KWS achieves classifier size of 6.39k parameters with$1.258~\mu $W power consumption and 34.625 ms latency. The accuracy is maintained between 87.5% – 92.2% for SNR in the range of 5 dB - 20 dB, which is one of the highest compared to the other state-of-the-art designs.