Single-Pass Covariance Matrix Calculation On A Hybrid Fpga/Cpu Platform

Covariance matrices are used for a wide range of applications in particle physics, including Kalman filter for tracking purposes or Primary Component Analysis for dimensionality reduction. Based on a novel decomposition of the covariance matrix, a design that requires only one pass of data for calculating the covariance matrix is presented. Two computation engines are used depending on parallelizability of the necessary computation steps. The design is implemented onto a hybrid FPGA/CPU system and yields speed-up of up to 5 orders of magnitude compared to previous FPGA implementation.