@inproceedings {27,
	title = {A Configurable 12-to-237KS/s 12.8 mW Sparse-approximation Engine for Mobile ExG Data Aggregation},
	year = {2015},
	month = {Feb.},
	pages = {68-78},
	publisher = {IEEE},
	abstract = {<p>Compressive sensing (CS) is a promising solution for low-power on-body sensors for 24/7 wireless health monitoring. In such an application, a mobile data aggregator performing real-time signal reconstruction is desired for timely prediction and proactive prevention. However, CS reconstruction requires solving a sparse approximation (SA) problem. Its high computational complexity makes software solvers, consuming 2-50W on CPUs, very energy inefficient for real-time processing. This paper presents a configurable SA engine in a 40nm CMOS technology for energy-efficient mobile data aggregation from compressively sampled biomedicai signals. Using configurable architecture, a 100\% utilization of computing resources is achieved. An efficient data-shuffling scheme is implemented to reduce memory leakage by 40\%. At the minimum-energy point (MEP), the SA engine achieves a real-time throughput for reconstructing 61-to-237 channels of biomedicai signals simultaneously with \&lt;;1\% of a mobile device\&$\#$39;s 2W power budget, which is 76-350\&times; more energy-efficient than prior hardware designs.</p>
},
	keywords = {psclab},
	url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7063062},
	author = {Ren, Fengbo and Markovi{\'c}, Dejan}
}
