2 Context-aware hierarchical information-sensing in a 6W 90nm CMOS voice activity detector

The rise of always-listening sensors integrated in energy-scarce devices such as watches and remote-controls increases the need for intelligent scalable interfaces. Contemporary sensor interfaces digitize raw sensor data to extract information with energy-intensive computations, such as FFT, which is inefficient if the end goal is to only extract selective information for classification tasks, e.g. voice activity detection (VAD). Previous work shows energy gains from early data reduction through analog feature extraction 1 or embedded classification hardware 2. However, the potential energy savings of these devices is limited as they cannot adapt to changes in the sensed information content or sensing context, such as the amount/type of acoustic background noise. In the processor design community, such adaptivity to varying operating conditions is actively researched through the concept of hierarchical computing 3. This work integrates the concept of hierarchical operation with adaptive early data extraction and classification, towards a power- and context-aware information-extraction sensor interface. This paper specifically reports on a μW 90nm CMOS VAD, that dynamically adapts sensing resources to signal information content and context, thus only spending energy on relevant information extraction. An order of magnitude in power savings is achieved by exploiting hierarchical sensing, run-time activated/scalable analog feature extraction and tightly-integrated context-aware mixed-signal machine learning inference, enabling novel applications in area of acoustic sensing 1,4.