Approximate Compressed Sensing: Ultra-low power biosignal processing via aggressive voltage scaling on a hybrid memory multi-core processor
Approximate Compressed Sensing: Ultra-low power biosignal processing via aggressive voltage scaling on a hybrid memory multi-core processor
复制标题
近似压缩传感：通过混合内存多核处理器上的积极电压调节进行超低功耗生物信号处理
DOI：
--
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
L. Benini
中科院分区：
文献类型：
--
作者：
Daniele Bortolotti;Hossein Mamaghanian;Andrea Bartolini;M. Ashouei;J. Stuijt;David Atienza Alonso;P. Vandergheynst;L. Benini
技术缩放使得能够设计适合新兴无线身体区域感测应用的低成本生物信号处理芯片。能量消耗严重限制了这些应用，并且存储器正在成为实现超低功耗操作的能量瓶颈。当使用积极的电压缩放时，由于缺乏足够的静态噪声容限，存储器操作变得不可靠。本文介绍了一种近似的生物信号压缩感知方法。我们提出了一种数字架构，其特征在于混合存储器（6 T-SRAM/SCMEM单元）设计用于控制特定数据结构上的扰动。结合统计上鲁棒的重建算法，该系统容忍内存错误，并以低面积开销实现显著的节能。
Technology scaling enables the design of low cost biosignal processing chips suited for emerging wireless body-area sensing applications. Energy consumption severely limits such applications and memories are becoming the energy bottleneck to achieve ultra-low-power operation. When aggressive voltage scaling is used, memory operation becomes unreliable due to the lack of sufficient Static Noise Margin. This paper introduces an approximate biosignal Compressed Sensing approach. We propose a digital architecture featuring a hybrid memory (6T-SRAM/SCMEM cells) designed to control perturbations on specific data structures. Combined with a statistically robust reconstruction algorithm, the system tolerates memory errors and achieves significant energy savings with low area overhead.