Context-Aware Hierarchical Information-Sensing in a 6µW 90nm CMOS Voice Activity Detector

📋 论文概要

该论文提出了一种上下文感知的分层信息感知架构，用于实现超低功耗的语音活动检测（VAD）。通过根据语音活动状态动态调整处理层次，并在早期进行模拟特征提取和简单分类，显著减少了能量密集型的数字化和FFT计算，在90nm CMOS工艺下实现了6µW的总功耗。

💡 主要创新点

• 提出了上下文感知的分层信息感知方法，根据语音活动状态动态调整处理层次，仅在有语音时启用高精度处理，节省功耗。
• 在模拟域进行早期特征提取和简单分类，减少了后续数字处理的数据量，避免了全数字化和FFT等高能耗计算。
• 实现了极低功耗（6µW）的完整语音活动检测系统，适用于始终监听的IoT设备。

🏷 关键词

📄 原文摘要

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

👥 作者与机构

Komail Badami, Steven Lauwereins, Wannes Meert, Marian Verhelst

KU Leuven, Leuven, Belgium