But we also have the V1 TPU paper and can see the TPUs are able to use less joules per inference compared to an older Nvidia architecture. Was not that close. Just makes sense Google V2 TPUs would do the same.
Hope Google does a V3 TPU and then will share a V2 TPU paper like they did on V1 of the TPUs.
What is far more impressive of the TPUs is
https://cloudplatform.googleblog.com/2018/03/introducing-Clo...
If really doing 16k a second through a NN and at a price you can offer generally now that is incredible. I want this paper even more so.
These costs also ignore transferring and storing massive data sets in the cloud. In general the cloud is a huge pain and I'd avoid it like the plague unless I was caught and really, really needed the scalability. But even then that only works if you have a scalable implementation of the algorithm you are working on.
Yes I can say it is a lot cheaper. That is what this article is all about.
You can do about twice the images per dollar using the TPUs with GCP versus using Nvidia with AWS.
Or what am I missing?
BTW, Google has released to the general public. What are you talking about?
"Google’s AI chips are now open for public use"
https://venturebeat.com/2018/02/12/googles-ai-chips-are-now-...