A 0.62mW Ultra-Low-Power Convolutional-NeuralNetwork Face-Recognition Processor and a CIS Integrated with Always-On Haar-Like Face Detector

📋 论文概要

提出了一款0.62mW超低功耗卷积神经网络人脸识别处理器，集成了CMOS图像传感器和持续运行的Haar特征检测，用于可穿戴设备的人脸识别和用户认证，解决了功耗与识别准确率的矛盾。

💡 主要创新点

• 首次将超低功耗CNN人脸识别处理器与CIS集成，实现always-on人脸检测与识别。
• 采用Haar级联预筛选与CNN分类两级流水线，大幅降低系统功耗至0.62mW。

🏷 关键词

📄 原文摘要

for the next-generation UI/UX of wearable devices. A FR system, shown in Fig. 14.6.1, was developed as a life-cycle analyzer or a personal black box, constantly recording the people we meet, along with time and place information. In addition, FR with always-on capability can be used for user authentication for secure access to his or her smart phone and other personal systems. Since wearable devices have a limited battery capacity for a small form factor, extremely low power consumption is required, while maintaining high recognition accuracy. Previously, a 23mW FR accelerator [1] was proposed, but its accuracy was low due to its hand-crafted feature-based algorithm. Deep learning using a convolutional neural network (CNN) is essential to achieve high accuracy and to enhance device

👥 作者与机构

Kyeongryeol Bong, Sungpill Choi, Changhyeon Kim, Sanghoon Kang,

Youchang Kim, Hoi-Jun Yoo KAIST, Daejeon, Korea Recently, face recognition (FR) based on always-on CIS has been investigated