As wireless communication systems grow more complex, especially with the advent of 5G networks, the demand for efficient digital predistortion (DPD) techniques to improve the linearity and performance of power amplifiers (PAs) is increasing. Advanced neural network hardware accelerators like EdgeDRNN offer promising solutions to these challenges by enhancing computational efficiency and accuracy. This thesis provides a detailed evaluation of the EdgeDRNN hardware accelerator, which is applied by the OpenDPD network to tackle the common issues of computational complexity and parameter management that often impede DPD implementations. By employing high-level synthesis (HLS) for the linear layer within the EdgeDRNN model, the performance metrics can be improved. Through our research, we estimate the performance of the combined system, which has a latency of 6.50 microseconds and a throughput of 0.26 giga-operation per second. The study highlights EdgeDRNN’s performance and scalability, particularly in its ability to adapt to future wireless standards like 5G. However, the research also identifies some limitations on the performance, which sets a stage for future exploration. By designing and optimizing a new hardware accelerator for DPD, this work enables ultra-high-speed DPD solutions for next-generation wireless communication systems.