Hanna Zbroszczyk

This paper focuses on the problem of high-performance streaming random number generation in the range of uniform and normal distributions in FPGAs. Our work is focused on lightweight implementation, suitable for a wide range of FPGAs. First, we review the existing types of random generation modules. Next, in this paper we present the construction of the designed generator. We divide it into two sections: Stream Uniform Numbers Generator Implementation and Cumulative Distribution-Based Stream Gaussian Generator. Each design step was verified in the scope of the quality of the output data, especially regarding the produced distributions. The results obtained are compared with existing solutions. We mainly consider resource utilization and throughput. We also add our quality factor, which is an effective utilization of FPGAs. Despite quality results, our modules were implemented using a high-level synthesis language (C/C++), contrary to typical hardware description level (HDL) approaches. It provides the opportunity to implement the proposed algorithms on CPUs. It was tested with positive results, thus highlighting the versatility of the solution that is unavailable in terms of HDL implementations. Our designed generators were confirmed to stand out for their satisfactory performance while occupying low logical resources.