There’s no real stack without an NDA, if you want to write code for the device itself. Clang does support the SHAVE cores they use, but I don’t know what the to

There’s no real stack without an NDA, if you want to write code for the device itself. Clang does support the SHAVE cores they use, but I don’t know what the tooling actually looks like. Probably much like any other cross compilation tool chain. If you just want to use it, there’s a Python SDK which you can use to run a subset of models popular frameworks can train. In that case it’s pretty easy to get going. I believe there’s also a C++ integration of some sort, but I haven’t used it myself. It’s all moot, though, because properly programmed, recent ARM CPUs are fast enough to run usable models such as MobileNet and ShuffleNet with good performance. The problem is that at the moment this draws anywhere between 4-12W if things are running full blast, at least until throttling kicks in. Movidius is more economical in terms of joules per inference, but it also costs quite a bit, and it’s not built in, so market penetration is non-existent. As I mentioned, though, this should start being less of an issue with the advent of cheap, built in tensor acceleration IP.