A 1.5µW End-to-End Keyword Spotting SoC with Content-Adaptive Frame Sub-Sampling and Fast-Settling Analog Frontend

📋 论文概要

本文提出了一款1.5µW端到端关键词唤醒SoC，通过内容自适应帧子采样和快速建立模拟前端技术，实现了AFE、FE和NN三个模块的协同优化，显著降低了系统功耗，适用于边缘物联网设备的低功耗唤醒应用。

💡 主要创新点

• 提出了内容自适应帧子采样技术，根据输入语音内容动态调整帧速率，减少不必要的计算和功耗。
• 设计了快速建立模拟前端（AFE），缩短建立时间，降低AFE的平均功耗。
• 实现了AFE、FE和NN的端到端协同优化，总功耗仅1.5µW，兼顾低功耗与唤醒精度。

🏷 关键词

📄 原文摘要

a wake-up mechanism for edge IoT devices. While recent advances in deep learning have improved KWS accuracy [1], reducing system power consumption remains a challenge. A typical KWS signal chain consists of an analog frontend (AFE), feature extractor (FE), and neural network classifier (NN). To reduce total KWS power, all three blocks must be carefully co-optimized. Recent KWS work reported 0.51µW consumption for the FE and NN, but it only supports two keywords and lacks an AFE, whose power often dominates [2]. A KWS system including an AFE was also proposed but consumes 16µW [3]. This work proposes a fully integrated keyword spotting system that employs the skip RNN algorithm [4] to simultaneously reduce the power consumption of the AFE, FE, and NN by adaptively sub-sampling (i.e.,

👥 作者与机构

Ji-Hwan Seol1,2, Heejin Yang1, Rohit Rothe1, Zichen Fan1, Qirui Zhang1,

Hun-Seok Kim1, David Blaauw1, Dennis Sylvester1 University of Michigan, Ann Arbor, MI Samsung Electronics, Hwasung, Korea 1 2 Keyword spotting (KWS) has become essential as