This work presents an ultra-low-power classifier that can be integrated within energy-constrained bio-sensors to enable rapid analysis for continuous health monitoring. The in-sensor classifier saves significant transmission energy by extracting critical information locally to eliminate the need of transmitting raw data to centralized servers for remote signal processing. The convolutional-neural-network (CNN)-based classifier is built by using reconfigurable delay-locked loops (DLLs) to carry out classification algorithms with time-domain multiply-accumulate (MAC) operations. Pseudo sigmoid activation functions are realized by regenerative comparators that transform weighted timing to probabilities. The presented classifier achieves low-power consumption of 240.34 nW while performing up to 20 k operations per second. The proposed time-domain classifier reduces the energy to 36% of the previous works.

这项工作提出了一种超低功耗分类器，该分类器可以集成到能量受限的生物传感器中，从而能够进行快速分析以进行持续的健康监测。传感器内分类器通过本地提取关键信息来节省大量传输能量，从而消除了将原始数据传输到集中式服务器进行远程信号处理的需求。基于卷积神经网络（CNN）的分类器是通过使用可重新配置的延迟锁定环（DLL）来执行具有时域乘法累加（MAC）操作的分类算法而构建的。伪Sigmoid激活函数由可将加权时序转换为概率的再生比较器实现。提出的分类器可实现240.34 nW的低功耗，同时每秒执行多达20 k次操作。