Accelerating transfer entropy computation

Abstract

Transfer entropy is a measure of information transfer between two time series. It is an asymmetric measure based on entropy change which only takes into account the statistical dependency originating in the source series, but excludes dependency on a common external factor. Transfer entropy is able to capture system dynamics that traditional measures cannot, and has been successfully applied to various areas such as neuroscience, bioinformatics, data mining and finance. When time series becomes longer and resolution becomes higher, computing transfer entropy is demanding. This paper presents the first reconfigurable computing solution to accelerate transfer entropy computation. The novel aspects of our approach include a new technique based on Laplace's Rule of Succession for probability estimation; a novel architecture with optimised memory allocation, bit-width narrowing and mixed-precision optimisation; and its implementation targeting a Xilinx Virtex-6 SX475T FPGA. In our experiments, the proposed FPGA-based solution is up to 111.47 times faster than one Xeon CPU core, and 18.69 times faster than a 6-core Xeon CPU.