It feels like over-engineering to me and I am skeptical about this for several reasons:


2.2x perf improvements with LLVM on CPU vs hand-tuned OpenBLAS and NNPACK? Even D Mir GLAS only claims MKL-like performance


How does the compiler deal with dynamic graph/variable sized input.


It seems like the framework is for production but if not:


How often do we need to compile? That was already a pain point in Theano.


What’s the build toolchain like? Bazel for Tensorflow is already a huge pain (especially in containers) and this seems even more complicated.


I see Jenkins/Travis tests but what is the coverage for ONNX?


(Note that i’m asking OP to answer, I’m just raising concerns)
Edit: Would be interested to know what did they choose as default layout: CHWN (Neon), NCHW (Caffe / Torch), NHWC (Tensorflow) as it has implications in convolutions performance and algorithm choice.