Design Considerations for Ultra-Low Energy Wireless Microsensor Nodes
IEEE Transactions on Computers
An Ultra Low Power System Architecture for Sensor Network Applications
Proceedings of the 32nd annual international symposium on Computer Architecture
RTAS '11 Proceedings of the 2011 17th IEEE Real-Time and Embedded Technology and Applications Symposium
A Low Power Wake-Up Circuitry Based on Dynamic Time Warping for Body Sensor Networks
BSN '11 Proceedings of the 2011 International Conference on Body Sensor Networks
Instruction set extensions for dynamic time warping
Proceedings of the Ninth IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis
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Movement monitoring using wearable computers has been widely used in healthcare and wellness applications. To reduce the form factor of wearable nodes which is dominated by battery size, ultra-low power signal processing is crucial. In this paper, we propose an architecture that can be viewed as a hardware accelerator and employs dynamic time warping (DTW) in a hierarchical fashion. The proposed architecture removes events that are not of interest from the signal processing chain as early as possible, deactivating all remaining modules. We consider tunable parameters such as sampling frequency and bit resolution of the incoming sensor readings for DTW to balance the power consumption and classification precision trade-off. We formulate a methodology for determining the optimal set of tunable parameters and provide a solution using Active-set algorithm. We synthesized the architecture using 45nm CMOS and illustrated that a three-tiered module achieves 98% accuracy with a power budget of 1.23μW, while a single level DTW consumes 6.3μW with the same accuracy. We furthermore propose a fast approximation methodology that runs 3200 times faster while introducing less than 3% error over the original optimization for determining the total power consumption.