FPGAs are theoretically better than GPUs to deploy Deep Learning models simply because they are theoretically better than anything at doing anything. In practice, though, you never have enough circuitry on an FPGA to efficiently deploy a large model, and they are not targetted by the main Deep Learning libraries so you have to do the whole thing by hand including quantizing your model, extracting its weights, coding each layer in embedded C/VHDL/etc, and doing most of the hardware optimization by hand. It is tedious enough for preferring plug-and-play solutions like GPUs/TPUs in most cases, including embedded systems.