Resumen
This paper proposes a Field-Programmable Gate Array (FPGA)-based hardware accelerator for assisting the embedded MicroBlaze soft-core processor in calculating population count. The population count is frequently required to be executed in cyber-physical systems and can be applied to large data sets, such as in the case of molecular similarity search in cheminformatics, or assisting with computations performed by binarized neural networks. The MicroBlaze instruction set architecture (ISA) does not support this operation natively, so the count has to be realized as either a sequence of native instructions (in software) or in parallel in a dedicated hardware accelerator. Different hardware accelerator architectures are analyzed and compared to one another and to implementing the population count operation in MicroBlaze. The achieved experimental results with large vector lengths (up to 217) demonstrate that the best hardware accelerator with DMA (Direct Memory Access) is ~31 times faster than the best software version running on MicroBlaze. The proposed architectures are scalable and can easily be adjusted to both smaller and bigger input vector lengths. The entire system was implemented and tested on a Nexys-4 prototyping board containing a low-cost/low-power Artix-7 FPGA.