At a basic level I think making new powerful technology accessible to more people is on average strongly positive. There are various efforts making good progress to address different parts of deep learning accessibility such as Keras (developer-friendly Python API), OpenAI (open basic research & safe AI), fast.ai (practical training for developers), etc. I'm a fan of all of that work. PlaidML is the company's contribution to making adoption easier.
For the purposes of proliferation and democratization making deep learning work on the most readily available hardware helps people get started with less friction. PlaidML is a step in that direction. It's fully open source and you can right now 'pip install' it on Mac/Win/Linux with Intel/AMD/NVIDIA GPU and have a Keras net running in a couple minutes. There are certainly warts and some missing features but as far as I know it's the only one an ordinary practitioner can use right now.
From a "what problem does this solve" standpoint PlaidML is most similar to Tensor Comprehensions and TVM. Each makes different tradeoffs but might eventually be able to share components like code generation for OpenCL, LLVM, etc. Layers like XLA, nGraph, ONNX, NNVM, etc, you can mostly think of as being stacked on top (they are ways to talk to lower layer runtimes like PlaidML). For example it would be reasonable for a future version of PlaidML to support TensorFlow integration via XLA or deployment of ONNX models on OpenCL-capable GPUs.
Anyway, I personally care most about what people can use. There's a cute demo that will run the pre-trained Keras examples against images from your webcam on your local GPU. It's quick to try and can serve as the basis for prototyping a real application: https://github.com/plaidml/plaidvision
