Ultra-Low Power Dynamic Knob in Adaptive Compressed Sensing Towards Biosignal Dynamics

IEEE Transactions on Biomedical Circuits and Systems
Aosen WangWenyao Xu

Abstract

Compressed sensing (CS) is an emerging sampling paradigm in data acquisition. Its integrated analog-to-information structure can perform simultaneous data sensing and compression with low-complexity hardware. To date, most of the existing CS implementations have a fixed architectural setup, which lacks flexibility and adaptivity for efficient dynamic data sensing. In this paper, we propose a dynamic knob (DK) design to effectively reconfigure the CS architecture by recognizing the biosignals. Specifically, the dynamic knob design is a template-based structure that comprises a supervised learning module and a look-up table module. We model the DK performance in a closed analytic form and optimize the design via a dynamic programming formulation. We present the design on a 130 nm process, with a 0.058 mm (2) fingerprint and a 187.88 nJ/event energy-consumption. Furthermore, we benchmark the design performance using a publicly available dataset. Given the energy constraint in wireless sensing, the adaptive CS architecture can consistently improve the signal reconstruction quality by more than 70%, compared with the traditional CS. The experimental results indicate that the ultra-low power dynamic knob can provide an effective adap...Continue Reading

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Citations

May 11, 2017·IEEE Transactions on Biomedical Circuits and Systems·Haipeng PengDaoshun Wang
Mar 6, 2018·IEEE Journal of Biomedical and Health Informatics·Chen SongWenyao Xu
Aug 28, 2021·Entropy·Jianming Wang, Jianhua Chen

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