NPU support: is currently very limited, defined on what is accessible through DirectML. It is basically limted to intel for now, as reported here. But with this being steadily improved, I am confident we will soon get wider support.
If you are interested in SNPE, you should definitively have a look at Qualcomm’s runtime here. I never used it myself, but Qualcomm did a great job in opening up NNE for mobile.
NNERuntimeORT upgrade: Yes, we try to increase the upgrade cadence to have more recent versions in future releases. Apologies for the delays.
The RDG interface: It will not be as easy as the GPU or CPU interface. The CPU and GPU interface are very close to what people are used from training environments like torch or TF. To do in-frame inference you need to know a little more details about Unreal, as it is very engine specific. In the short: The inference call EnqueueRDG will need you to pass a Render Dependency Grah builder, which you have access to e.g. when you add your own ViewExtensionBase class for e.g. post processing. So the interface is more powerful, as you run inference on frame resources without any CPU sync, but it needs a little more knowledge about UE