Outstanding seizure detection algorithms using electroencephalogram (EEG) recordings have been developed over the past decade. These works mainly focus on best of class performance, which leads to computationally heavy solutions. This limits the applicability of these detection algorithms for hardware implementations such as field­programmable gate arrays (FPGAs). Which in turn limits its use in real­world applications such as warning systems or neural­stimulation systems. In this work, a convolutional neural network (CNN) is trained and its properties reduced to minimize its footprint on hardware. The network is trained based on 1423 EEG recording sessions of 637 different patients sourced from the TUSZ epilepsy database. The input EEG data is processed by removing artifacts, then applying a short­term Fourier transform (STFT) and normalizing and quantizing this to 1 byte values. The neural network is optimized by reducing the input space, this is done by reducing the number of channels, time, and frequencies used. The CNN itself is reduced by quantizing the neural network and reducing its size. Based on these results two ANNs are selected and implemented on an FPGA, one is optimized for accuracy and one for network size. The resulting networks have a sensitivity of 72.26% and 70.00% and an MCC­score of 0.711 and 0.591. The first FPGA implementations consume 0.296 W and 0.562 W at 50000 detections per second.