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“The TPU hardware is not specific to TensorFlow in any way.”

Mschmit
For Google’s specific TPU I do not think this is correct. That’s why Google and everyone else has labeled it as an ASIC processor. The application it is specific to is Tensorflow.

For instance here is how google markets the TPU on to their cloud customer.
“You can use a Cloud TPU to accelerate specific TensorFlow machine learning workloads. This page explains how to create a Cloud TPU and use it to run a basic TensorFlow model.” This from the how to get started section of computing with TPU.

https://cloud.google.com/tpu/docs/quickstart

The math equations that you reference are correct in terms of thinking about what “Tensor” is but not so much in thinking about what a Google TPU is. It’s an ASIC that features tensor core architecture. The programming and architecture is of an ASIC. Yes Tensorflow can integrate with many different programs but if you want to use the Google TPU to accelerate you must use Tensorflow. At least you have to set up your machine learning task through the Tensorflow application to use Google’s Cloud TPU service as it is currently set up. Maybe I’m wrong and it would be able to perform accelerations on other applications after beta. Do you have eveidence otherwise?
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