Google supercharges machine learning tasks with TPU custom chip

Re:Google supercharges machine learning tasks with TPU custo... by randyrand on Wednesday May 18, 2016 @06:05PM (#11726379)

I'm not even a hardware or AI person, and even I could have told you ASICs would make way more sense than GPUs or FPGAs for machine learning. It's all about data locality. Fetching memory is the most costly thing a GPU does, and for ML (DNNs) there no big need for global access memory 99% of the time.

Anyone the casually follows AI knows that people have been talking about making DNN ASICS for some time. It was all a matter of time and $$$$$$

There is no doubt FB is working on them too. Which is why Google is finally publicaly saying that "we did it first ;)"

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Re:Google supercharges machine learning tasks with TPU custo... by mtgx on Wednesday May 18, 2016 @06:27PM (#11726522)

> It's all about data locality.

That's kind of what Movidius says, too, about its Myriad 2 VPU, which is kind of a GPU (SIMD-VLIW) with larger amounts of local memory combined with hardware accelerators.

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Re:Google supercharges machine learning tasks with TPU custo... by rdtsc on Wednesday May 18, 2016 @06:19PM (#11726479)

> Google is finally publicaly saying that "we did it first ;)"

Makes sense. In that respect yeah, they probably wanted to keep it under wraps to avoid Facebook/others from getting a timeline estimate out of it and jump ahead.

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