As data volume grows, computational speed becomes a key challenge. Data reduction helps address this by eliminating redundancy in rough sets using a reduct. However, most reduct-generation algorithms rely on software, which suffers from limitations like fixed word length and execution delays due to instruction processing, making them relatively slow. This paper proposes a hardware implementation of a two-stage greedy algorithm for reduct computation. The first stage identifies the core via a discernibility matrix, while the second enriches it with essential attributes. Presented algorithms were implemented on both Altera and Xilinx Field Programmable Gate Array (FPGA) units for high-speed, parallel processing and compared to a C-based software implementation on a PC. Results show a significant improvement in processing speed using the hardware approach.