Given the rapidly growing interest in neuromorphics and spike-based computation, there are a wide range of techniques, software frameworks, and hardware implementations that explore these ideas.  We have been integrating some of these approaches into a common software toolkit, Nengo, which provides a high-level programming interface for the specification of spike-based neural networks, and then compiles these models to target different hardware, including CPUs, GPUs, digital neuromorphics, and analog neuromorphics.  We will discuss some of the challenges involved in compiling to such a wide range of hardware, and show examples of efficiency gains both for neuroscientific modelling of large-scale biological systems and for modern machine-learning algorithms such as deep networks.