A 0.5V 54µW Ultra-Low-Power Recognition Processor with 93.5% Accuracy Geometric Vocabulary Tree and 47.5% Database Compression

📋 论文概要

本文提出一种超低功耗物体识别处理器，工作在0.5V电压下仅消耗54µW功耗。通过几何词汇树结构实现93.5%的识别准确率，并采用数据库压缩技术减少47.5%的存储开销，解决了物联网应用中连续视觉处理的高功耗与有限电池容量之间的矛盾。

💡 主要创新点

• 提出0.5V超低电压操作的物体识别处理器架构，大幅降低功耗至54µW。
• 采用几何词汇树（Geometric Vocabulary Tree）提升识别准确率达93.5%。
• 实现47.5%的数据库压缩率，减少存储需求。

🏷 关键词

📄 原文摘要

Microwatt object recognition is being considered for many applications, such as autonomous micro-air-vehicle (MAV) navigation, a vision-based wake-up or user authentication for the smartphones, and a gesture recognition-based natural UI for wearable devices in the Internet-of-Things (IoT) era. These applications require extremely low power consumption, while maintaining high recognition accuracy – constraints that arise because of the requirement for continuous heavy vision processing under limited battery capacity. Recently, a low-power feature-extraction accelerator operating at near-threshold voltage (NTV) was proposed, however, it did not support the object matching essential for the object recognition [1]. Even state-of-the-art object matching accelerators consume over 10mW, thereby making them unsuitable for an MAV [2, 3]. Therefore, an ultra-low-power high-accuracy recognition processor is

👥 作者与机构

Youchang Kim, Injoon Hong, Hoi-Jun Yoo

KAIST, Daejeon, Korea