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A 184-μW Error-Tolerant Real-Time Hand Gesture Recognition System With Hybrid Ti
提出一种低功耗实时手势识别系统，结合混合分类器和多数投票方案，显著提升识别精度。
65nm CMOS, 0.6V, 184μW @ 25MHz
手势识别低功耗实时处理混合分类器多数投票
▸采用计算高效的混合分类器结合多数投票方案
▸压缩输入数据减少内存和计算负载
▸Edge-CNN核心减少内存访问和特征寄存器切换率
Abstract
This article proposes a low-power real-time hand
gesture recognition (HGR) system with high recognition accuracy
for smart edge devices. This design balances accuracy and
power consumption by utilizing computation-efficient hybrid
classifiers assisted with a majority voting scheme. By combining
the recognition results of consecutive frames, the HGR system
shows improved immunity to misclassification. In addition, the
compressed input data before high-level processing dramatically
reduce the on-chip