A 90 nm CMOS, 6 μW Power-Proportional Acoustic Sensing Frontend for Voice Activity Detection

被引：89

作者：

Badami, Komail M. H. ^{[1
]}

Lauwereins, Steven ^{[1
]}

Meert, Wannes ^{[2
]}

Verhelst, Marian ^{[1
]}

机构：

[1] Katholieke Univ Leuven, Dept Elektrotech ESAT MICAS, B-3001 Louvain, Belgium

[2] Katholieke Univ Leuven, Dept Comp Sci, B-3001 Louvain, Belgium

来源：

IEEE JOURNAL OF SOLID-STATE CIRCUITS | 2016年 / 51卷 / 01期

关键词：

Acoustic frontend; analog machine learning; context-aware computing; hierarchical computing; scalable low power analog; voice activity detection (VAD); ANALOG; SYSTEM; END;

D O I：

10.1109/JSSC.2015.2487276

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This work presents a sub-6 mu W acoustic frontend for speech/non-speech classification in a voice activity detection (VAD) in 90 nm CMOS. Power consumption of the VAD system is minimized by architectural design around a new power-proportional sensing paradigm and the use of machine-learning-assisted moderate-precision analog analytics for classification. Power-proportional sensing allows for hierarchical and context-aware scaling of the frontend's power consumption depending on the complexity of the ongoing information extraction, while the use of analog analytics brings increased power efficiency through switching ON/OFF the computation of individual features depending on the features' usefulness in a particular context. The proposed VAD system reduces the power consumption by 10x as compared to state-of-the-art (SotA) systems and yet achieves an 89% average hit rate (HR) for a 12 dB signal-to-acoustic-noise ratio (SANR) in babble context, which is at par with software-based VAD systems.

引用

页码：291 / 302

页数：12