Hi-Tech

Second-generation Tensor Processing Units are coming to Google Cloud!

Second-generation Tensor Processing Units are coming to Google Cloud!

On the software side, Pichai said Google is working on developing neural nets capable of developing their own neural nets in an automated process he described as machines "learning to learn". They are designed for data centers and can be stacked to build "machine learning supercomputers" called TPU pods.

Machine learning has made rapid progress over the past few years.

Google first went with scaling its training capabilities however, in face of huge turnaround times, the company realized that it needed a new machine learning system - and that is exactly what it designed. I think the TPU is more specialized, so there are certain kinds of workloads, not necessarily machine learning ones but other kinds of workloads that run well on GPUs that don't necessarily run on a TPU.

We call them Cloud TPUs, and they will initially be available via Google Compute Engine.

Typically, this work is done using commercially available GPUs, often from Nvidia - Facebook uses Nvidia GPUs as part of its Big Basin AI training servers. The new chips almost double the speed of Google's previous version built in 2015, able to perform 180 teraflops (FLOPS, floating operations per second) against the previous generation's 92 teraflops. Google will of course use the system itself, but it is also billing the new TPU as an unrivaled resource for other companies to make use of. Second generation tensor processing units (TPUs) will work with TensorFlow, as well as CPU or GPU systems that are able to process AI models like Caffe2, Intel Skylake, or Nvidia's Volta.




Enlarge / A 64-chip TPU pod, for 11.5 petaflops of computation. Dean claims that only eight of the second-generation chips could train a Google translation program in an afternoon, where it had taken a full day for 32 graphics chips to train the same software.

New to the second generation TPUs is training, which feeds thousands of inputs into an algorithm to form new ways of performing tasks. Importantly, these new TPUs also now supports training as well as inference.

If most of this is gibberish for you, be not afraid.

Last month, Google published a paper comparing TPUs to existing chips and said its own processors are running 15 to 30 times faster and 30 to 80 times more efficient than the competition. "If we can get the time for each experiment down from weeks to days or hours, this improves the ability for everybody doing machine learning to iterate more quickly and do more experiments", Dean says. In addition, the company is launching a new TensorFlow Research Cloud that will provide researchers with free access to that hardware if they pledge to publicly release the results of their research. Google is offering free access to 1000 of its new TPUs to AI researchers who commit to publishing and open-sourcing their results.


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