[dgacmu]

It's possible, but I think that the majority of ML optimization as seen by a programmer using tensorflow is more about optimizing the balance of accuracy, training & inference speed, and memory use, and a lot of the solutions in this space are pretty hardware independent. There's an entire other type of optimization about, e.g., making conv2d insanely fast, but that's not something that a typical data scientist-type user deals with.

(To elaborate -- it's questions like "how deep should I make this convolution? Should I use tf.relu or tf.sigmoid? How many fully-connected layers should I put here, and how big should I make them?". These are really knotty deep learning design questions, but they're often h/w independent. Not always - we certainly have some ops on TF that we only support in CPUs and not on GPUs, for example - but often.)

[Retric]

I am more thinking in terms of:

  1. Best price/performance is tensorflow right now. So, the best software choice is platform X.  
  2. Then in 2 years.. Well we are using Platform X so tensorflow is clearly the best option.

In other words once you pick conv2d, you tend to also stick with whatever conv2d is optimized for. Which also means HW vendors love to help optimize popular platforms.

[visarga]

Others can make TF hardware too. Now that Google has opened the way, they will compete.