Compressed Sensing (CS) is a revolutionary technology for realizing low-power sensor nodes through sub-Nyquist sampling, and the CS reconstruction engines have been widely studied to fulfill the energy efficiency for real-time processing. However, in most cases, we only want to analyze the problematic signals which account for a very low percentage. Therefore, large efforts will be wasted if we recover uninterested signals. On the other hand, in order to identify the high-risk signals, additional hardware and computation overhead are required for classification other than CS reconstruction. In this paper, to achieve low-complexity on-demand CS reconstruction, we propose a two-stage classification-aided reconstruction (TS-CAR) framework. The compressed signals can be classified with a sparse coding based classifier, which provides the hardware sharing potential with reconstruction. Furthermore, to accelerate the reconstruction speed, a cross-domain sparse transform is applied from classification to reconstruction. TS-CAR is implemented in electrocardiography based atrial fibrillation (AF) detection. The average computational cost of TS-CAR is 2.25× fewer compared to traditional frameworks when AF percentage is among 10% to 50%. Finally, we implement TS-CAR in TSMC 40 nm technology. The post-layout results show that the proposed intelligent CS reconstruction engine can provide a competitive area- and energy-efficiency compared to state-of-the-art CS and machine learning engines.

中文翻译：

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压缩感知问题信号的低复杂度按需重建


压缩感知（CS）是一项通过亚奈奎斯特采样实现低功耗传感器节点的革命性技术，并且压缩感知重建引擎已被广泛研究以实现实时处理的能源效率。然而，在大多数情况下，我们只想分析占比很低的有问题的信号。因此，如果我们恢复不感兴趣的信号，那么巨大的努力就会被浪费。另一方面，为了识别高风险信号，除了CS重建之外，分类还需要额外的硬件和计算开销。在本文中，为了实现低复杂度的按需 CS 重建，我们提出了一种两阶段分类辅助重建（TS-CAR）框架。压缩信号可以使用基于稀疏编码的分类器进行分类，这提供了重建的硬件共享潜力。此外，为了加快重建速度，从分类到重建应用了跨域稀疏变换。 TS-CAR 应用于基于心电图的心房颤动 (AF) 检测。当AF百分比在10%到50%之间时，TS-CAR的平均计算成本比传统框架低2.25倍。最后，我们在台积电 40 nm 技术中实现了 TS-CAR。布局后结果表明，与最先进的 CS 和机器学习引擎相比，所提出的智能 CS 重建引擎可以提供具有竞争力的面积和能源效率。