A Low-Power Microcontroller with Accuracy-Controlled Event-Driven Signal Processing Unit for Rare-Event Activity-Sensing IoT Devices
A Low-Power Microcontroller with Accuracy-Controlled Event-Driven Signal Processing Unit for Rare-Event Activity-Sensing IoT Devices
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DOI：
10.1155/2015/809201
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发表时间：
2015-01-01
影响因子：
1.9
通讯作者：
Cho, Jeonghun
中科院分区：
文献类型：
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作者：
Park, Daejin;Youn, Jonghee M.;Cho, Jeonghun
提出了一种专门设计的带有事件驱动传感器数据处理单元(EPU)的微控制器，为罕见事件人类活动传感应用中的物联网设备提供节能的传感器数据采集。使用远程安装物联网传感器设备的罕见事件传感应用具有事件到事件距离很长的特性，因此传感器数据在一定精度误差范围内的不准确处理足以从收集的传感数据中提取合适的事件。提出的信号-事件转换器(S2E)作为传统传感器接口的预处理器，提取一组具有感兴趣的特定特征的原子事件，并对输入传感器信号的特征点进行早期评估。提出的事件处理单元(EPU)可以完成常规的传感器数据处理，如数字信号处理器或软件驱动的算法，从采集的传感器数据中分类有意义的事件。所提出的微控制器体系结构能够为罕见事件检测应用提供高能效的信号处理。所实现的系统级芯片(SoC)包括所提出的构建模块，采用0.18um CMOS工艺，增加了7500个NAND门和1KB SRAM跟踪器，与传统的用于手势检测的传感器数据处理方法相比，功耗仅为20%。
A specially designed microcontroller with event-driven sensor data processing unit (EPU) is proposed to provide energy-efficient sensor data acquisition for Internet of Things (IoT) devices in rare-event human activity sensing applications. Rare-event sensing applications using a remotely installed IoT sensor device have a property of very long event-to-event distance, so that the inaccurate sensor data processing in a certain range of accuracy error is enough to extract appropriate events from the collected sensing data. The proposed signal-to-event converter (S2E) as a preprocessor of the conventional sensor interface extracts a set of atomic events with the specific features of interest and performs an early evaluation for the featured points of the incoming sensor signal. The conventional sensor data processing such as DSPs or software-driven algorithm to classify the meaningful event from the collected sensor data could be accomplished by the proposed event processing unit (EPU). The proposed microcontroller architecture enables an energy efficient signal processing for rare-event sensing applications. The implemented system-on-chip (SoC) including the proposed building blocks is fabricated with additional 7500 NAND gates and 1-KB SRAM tracer in 0.18 um CMOS process, consuming only 20% compared to the conventional sensor data processing method for human hand-gesture detection.