Histogram-based probability density function estimation on FPGAs

Probability density functions (PDFs) have a wide range of uses across an array of application domains. Since computing the PDF of real-time data is typically expensive, various estimations have been devised that attempt to approximate the real PDFs based on fitting data to an expected underlying distribution. As we move to more adaptive systems, real-time monitoring of signal statistics increases in importance. In this paper, we present a technique that leverages the heterogeneous resources on modern FPGAs to enable real time computation of PDFs of sampled data at speeds of over 200 Msamples per second. We detail a flexible architecture that can be used to extract statistical information in real time while consuming a moderate amount of area, allowing it to be incorporated into existing FPGA-based applications.