Available at: http://digitalcommons.calpoly.edu/theses/1038
Date of Award
MS in Electrical Engineering
Microcontrollers, the brains of embedded systems, have found their way into every aspect of our lives including medical devices such as pacemakers. Pacemakers provide life supporting functions to people therefore it is critical for these devices to meet their timing constraints. This thesis examines the use of hardware co-processing to accelerate the calculation time associated with the critical tasks of a pacemaker. In particular, we use an FPGA to accelerate a microcontroller’s calculation time of the Kendall Tau Rank Correlation Coefficient algorithm. The Kendall Tau Rank Correlation Coefficient is a statistical measure that determines the pacemaker’s voltage level for heart stimulation. This thesis explores three different hardware distributions of this algorithm between an FPGA and a pacemaker’s microcontroller. The first implementation uses one microcontroller to establish the baseline performance of the system. The next implementation executes the entire Kendall Tau algorithm on an FPGA with varying degrees of parallelism. The final implementation of the Kendall Tau algorithm splits the computational requirements between the microcontroller and FPGA. This thesis uses these implementations to compare system-level issues such as power consumption and other tradeoffs that arise when using an FPGA for co-processing.