To expand on this a bit, NNVM is mostly a graph serialization format and graph optimizer with a cuda/cudnn (and now TVM) backend. In this NNVM is very similar to XLA. Our approach handles both full graph optimization (though we have a lot of work to do there) and kernel creation and optimization through an intermediate language called Tile. TVM seems somewhat derivative of our approach, though it lacks a reasonable mechanism for optimizing kernels.

PlaidML and Tile are able to create optimal kernels for just about any architecture. This approach reduces dependencies and ensure that new hardware will just work.

We intend to have NNVM and Tensorflow backends in the future. The keras backend is only 2000 lines of code (thanks to tile).