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Traditionally, network intrusion detection systems identify attacks based on signatures, rules, events or anomaly detection. More and more research investigates the application of deep learning techniques for this purpose. Deep learning significantly increases detection performance, and can abolish the need for expert knowledge-intensive feature extraction. The use of deep learning for network intrusion detection also has a major disadvantage, however, as it is not deployed yet in real-time implementations. In this paper, we propose two approaches that facilitate the transition towards functional real-time implementations: (1) the use of flow buckets to collect raw traffic-based features, and (2) the acceleration of neural network architectures for intrusion detection using the Xilinx FINN toolchain for FPGAs. We obtain promising results that show our flow bucket approach does not deteriorate detection performance when compared to traditional approaches, and we lay a foundation to further build on with respect to accelerating deep learning algorithms for network intrusion detection on FPGA.