Brain-inspired Spiking Neural Networks (SNNs) are a promising and energy-efficient alternative to standard Artificial Neural Networks (ANNs) and are particularly well-suited to spatio-temporal tasks such as keyword spotting and video classification. However, SNNs have much lower arithmetic intensity than ANNs and are therefore not well matched to standard accelerators such as GPUs and TPUs. In this talk, we will discuss why FPGAs are a pragmatic choice for accelerating memory-bound workloads like SNNs, give details on how we tailored our design to the resources available on modern FPGAs, present recent results, and discuss our plans for the future.