Science

Google's new TPUs are here to accelerate AI training

Google's new TPUs are here to accelerate AI training

Much like Nvidia has built servers out of multiple V100s, Google has also constructed TPU Pods that combine multiple TPUs to achieve 11.5 petaflops (11,500 teraflops) of performance. Each pod contains 64 second generation TPUs, providing up to 11.5PFLOPs. Even on their own, the second-gen TPUs are capable of "delivering a staggering 180 teraflops of computing power and are built for just the kind of number crunching that drives machine learning today", says Fei-Fei Lee, Google's chief scientist of AI and machine learning. "You'll be able to mix-and-match Cloud TPUs with Skylake CPUs, NVIDIA GPUs, and all of the rest of our infrastructure and services to build and optimize the flawless machine learning system for your needs".

At Google I/O 2017, Google announced its next-generation machine learning chip, called the "Cloud TPU".

Deep learning, a trendy type of AI, typically involves two stages: training artificial neural networks on lots of data, and then directing the networks to make inferences about new data based on what it knows.

The first of Google's chips was only capable of using machine learning models to make decision.

Programming algorithms that run on TPUs will require the use of TensorFlow, the open source machine learning framework that originated at Google. Thanks to Google's advances and its integration of hardware and software, however, TensorFlow has emerged as one of the leading platforms on which to build AI software.




Making comparisons with other machine-learning solutions is hard. "We'll be bringing Cloud TPUs to the Google Compute Engine so that companies and developers can take advantage of it".

The edge this gives Google over competitors' offerings is the speed and freedom to experiment, says Jeff Dean, a senior fellow on the Google Brain team. The same job runs in six hours on one-eighth of a pod, presumably eight TPUs. The first generation TPU was only able to handle inference. The former workload is the one that is most heavily dependent on massive compute power, and it's this that has generally been done on GPUs.

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.

"Since fast [machine learning] accelerators place extraordinary demands on surrounding storage systems and networks, we're making optimisations throughout our Cloud infrastructure to help ensure that you can train powerful [machine learning] models quickly using real production data", they added.

In a post on its website, Google noted "While our first TPU was created to run machine learning models quickly ... training a machine is more hard than running it and days or weeks of computation on the best available CPUs and GPUs are commonly required to reach state of the art levels of accuracy".


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