Google has built arrays of these TPUs called "TPU pods", which contain 64 TPUs and are capable of delivering 11.5 petaflops of processing power.
Google is already investing in ML-specific hardware with its line of Tensor Processing Unit chips, which are created to accelerate both the training of new machine learning algorithms as well as data processing using existing models. "One of our new large-scale translation models used to take a full day to train on 32 of the best commercially-available GPUs-now it trains to the same accuracy in an afternoon using just one eighth of a TPU pod". However, if that amount of power is not enough, you can use multiple TPUs connected together - forming what Google calls TPU Pods.
Jeff Dean, a senior fellow for Google Brain, told reporters this week that Google is still using CPUs and GPUs to train some machine learning models.
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Google's making the Research Cloud available to accelerate the pace of machine learning research and plans to share it with entities like Harvard Medical School.
The massively powerful systems are built for machine learning and artificial intelligence, and Google is pushing it into the cloud with their TPU-based computational powerhouse systems to be made available to Google Cloud Compute later this year. The company also introduced a new initiative it is calling Google.AI, which will work to centralize all of the company's AI efforts under one roof. It's potentially troubling news for Nvidia, whose graphics processing units (GPUs) have been used by Google for intensive machine learning applications.
The TPU was first announced a year ago at the annual Google I/O event.
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Although still in its relative infancy, machine learning tools are already making promising strides in a number of fields, including medical research.
Google's cloud Tensor processing unit board. However, he expects that over time, Google will increasingly use TPUs. While Google's first-generation TPUs were designed only to carry out inference quickly, the newer variant is also geared towards accelerating the training of ML models. Google is offering free access to 1000 of its new TPUs to AI researchers who commit to publishing and open-sourcing their results. It is also allowing users to start building their models on competing chips like Intel's Skylake or GPUs like Nvidia's Volta and then move the project to Google's TPU cloud for final processing.
The merger of several groups under the Google.ai group certainly shows that the company is committed to its machine learning platform and that it views these technologies as a key part of its strategy going forward. Cloud TPUs are running now on the Google Compute Engine, Pichau added.
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