A Configurable 12–237 kS/s 12.8 mW Sparse-Approximation Engine for Mobile Data Aggregation of Compressively Sampled Physiological Signals
A Configurable 12–237 kS/s 12.8 mW Sparse-Approximation Engine for Mobile Data Aggregation of Compressively Sampled Physiological Signals
复制标题
用于压缩采样生理信号移动数据聚合的可配置 12–237 kS/s 12.8 mW 稀疏逼近引擎
DOI：
10.1109/jssc.2015.2480862
复制
发表时间：
2016
影响因子：
5.4
通讯作者：
D. Markovic
中科院分区：
文献类型：
--
作者：
Fengbo Ren;D. Markovic
压缩传感（CS）是一项有前途的技术，用于实现Pervasive Health Systems中24/7健康监测的无线无线传感器节点（WSN）。由于重建算法的高计算复杂性（CC），软件解决方案无法满足实时处理的能源效率需求。在本文中，我们提出了12.237 ks/s 12.8兆瓦稀疏 - 敏捷（SA）发动机芯片，该芯片能够在移动平台上进行压缩采样生理信号的节能数据聚集。 40 nm CMO集成的SA发动机芯片可以同时支持200多个生理信号渠道的同时重建（占智能手机的功率预算的1％）。这种能效重建可以在传感器节点的传感器节点上节省两到三倍的能量与传统的基于Nyquist的系统相比，基于CS的健康监测系统，同时提供及时的反馈并使信号智能更接近用户。
Compressive sensing (CS) is a promising technology for realizing low-power and cost-effective wireless sensor nodes (WSNs) in pervasive health systems for 24/7 health monitoring. Due to the high computational complexity (CC) of the reconstruction algorithms, software solutions cannot fulfill the energy efficiency needs for real-time processing. In this paper, we present a 12-237 kS/s 12.8 mW sparse-approximation (SA) engine chip that enables the energy-efficient data aggregation of compressively sampled physiological signals on mobile platforms. The SA engine chip integrated in 40 nm CMOS can support the simultaneous reconstruction of over 200 channels of physiological signals while consuming (1% of a smartphone's power budget. Such energyefficient reconstruction enables two-to-three times energy saving at the sensor nodes in a CS-based health monitoring system as compared to traditional Nyquist-based systems, while providing timely feedback and bringing signal intelligence closer to the user.