An ultra-low power hardware accelerator architecture for wearable computers using dynamic time warping

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.