A 13.5µW 35-Keyword End-to-End Keyword Spotting System Featuring Personalized On-Chip Training in 28nm CMOS

📋 论文概要

本文提出一个13.5µW、支持35个关键词的端到端关键词识别系统，在28nm CMOS工艺中实现。通过片上个性化训练，系统能够针对用户口音进行定制，解决了传统KWS系统功耗高且无法个性化适配的问题。

💡 主要创新点

• 首款集成了片上个性化训练的端到端KWS系统，允许针对用户口音实时调整识别模型。
• 在28nm CMOS工艺下实现13.5µW超低功耗，支持35个关键词，显著优于现有方案。

🏷 关键词

📄 原文摘要

customized to individual users. However, keyword spotting (KWS), a feature that is gaining widespread adoption in many personal devices, remains largely non-user-configurable as it is designed for the general public. While user-specific training can enable a personalized KWS system tailored to individual users’ accents, the large power consumption and high complexity required in training have hindered its adoption. Thus, existing KWS systems often suffer from missed commands, especially for users with accents, leading to increased energy consumption and an undesirable user experience. Furthermore, existing KWS systems usually recognize only a few words for waking up the device, although future devices are expected to understand a much broader vocabulary to execute various commands. In the recent past,

👥 作者与机构

Hyuk-Jin Lee1, Kyunghoon Pyo1, Taekwang Jang2, Mingoo Seok3, SeongHwan Cho1

KAIST, Daejeon, Korea ETH Zürich, Zürich, Switzerland 3 Columbia University, New York, NY 1 2 Modern IT devices offer personalized features that can be