Given the big-data regime in which we work today, real-time processing is a strict requirement to get the most out of data while it's available. Over the years, there have been many advances in processing power, from multi-core CPUs to off-the-shelf GPUs. However, both of these examples are best suited for certain situations, given their hardware architectures. FPGAs (field-programmable gate arrays), on the other hand, have an open, almost undefined architecture, allowing the user to define it, through the creation of image files that are flashed to the device. As well as having a highly-configurable architecture, FPGAs also use much less power than other accelerator boards like GPUs, while still delivering very good performance (e.g. 38 TFLOPS for an Intel Agilex 7), making FPGAs a useful resource when power is a consideration, or to reduce cooling requirements in high-packing-density situations. While these devices are becoming more commonplace and off-the-shelf versions are available, there is still some effort required to successfully integrate these seamlessly into a pipeline. In this talk, I will describe our efforts to integrate FPGAs into our real-time pipeline for pulsar and fast transient searching for the SKA (named cheetah), and show the performance benefits we have gained from doing so.