Presentation
An Energy-Efficient Kalman Filter Architecture with Tunable Accuracy for Brain-Computer Interfaces
DescriptionKalman Filter (KF) is the most prominent algorithm to predict motion from measurements of brain activity. However, little effort has been made to specialize KF hardware for the unique requirements of embedded brain-computer interfaces (BCIs). For this reason, we present the first configurable KF hardware architecture that enables fine-grained tuning of latency and accuracy, thereby facilitating specialization for neural data processing in BCI applications and supporting design-space exploration. Based on our architecture, we design KF hardware accelerators and integrate them into a heterogeneous system-on-chip (SoC). Through FPGA-based experiments, we demonstrate an energy-efficiency improvement of 15.3x and 10^3x better accuracy compared to state-of-the-art implementations.
Event Type
Research Manuscript
TimeMonday, June 235:15pm - 5:30pm PDT
Location3002, Level 3
Design
DES1: SoC, Heterogeneous, and Reconfigurable Architectures