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This work presents a sub-6 μW 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 10× as compared to state-of-the-art (SotA) systems and yet achieves an 89% average hit rate (HR) for a 12 dB signal-to …