This thesis investigates the use of radiation-hardened FPGAs for neural network implementation in radiation-prone environments. For this purpose, the European-based NG-Medium board is examined and compared to the non-radiation-hardened Pynq-Z2. A UART communication system and a neural network for color classification were designed to function on both boards without significant alterations to ensure equal compatibility for a fair comparison. The difference between the utilization and performance of the NG-Medium and the Pynq-Z2 FPGA is interesting. The Pynq-Z2 demands more resources for code execution, whereas the NG-Medium struggles with proper code functioning, possibly contributing to its seemingly lower consumption. The Pynq-Z2 also performs better in speed due to the faster clock frequency.