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Training ResNet with 8IPUs is roughly the same as 8GPUs. I doubt it was Tensor Core optimized so I assume the GPUs can go way faster.

I'd love to spend some time and come up with Graphcore optimized models. But at this stage fused Tensor Core optimized ops are much more interesting to me.

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Yep, nVidia CEO claimed nVidia is really a software company citing the complex software they built on top of their GPU.

But I think AI as a whole needs to make optimizing ML AI workloads on non GPU accelerators equally well. Otherwise the industry will be stifled by the money seeking shortsigtness of nVidia

Easy enough to optimize a net against a new architecture: https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creat...

Seems this is to using automl to find NN architecture that fits well in certain inference chips.

The orthogonal issue is given a trained model how to make it run faster on a given inference chip.

Either way, a blog post on Google AI is a symbol that 1% of the work is done, the rest 99% needs more investment from the believers.

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