title: Ultra-Low Power Dynamic Knob in Adaptive Compressed Sensing Towards Biosignal Dynamics.
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 adaptivity and improve the signal quality in compressed sensing towards biosignal dynamics.
authors.forename: Aosen
authors.initials: A
authors.lastname: Wang
authors.forename: Feng
authors.initials: F
authors.lastname: Lin
authors.forename: Zhanpeng
authors.initials: Z
authors.lastname: Jin
authors.forename: Wenyao
authors.initials: W
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D015374:Biosensing Techniques / Q000295:instrumentation*; D003195:Computer Communication Networks / Q000295:instrumentation*; D044962:Data Compression; D004867:Equipment Design; D006801:Humans; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation; D059015:Wireless Technology / Q000295:instrumentation*