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Google will put mini-TPUs into upcoming cellphones

19 May, 2017, 06:17 | Author: Nadine Todd
  • Google will put mini-TPUs into upcoming cellphones

At Google I/O today, Google CEO Sundar Pichai provided an update on next generation of the custom-made Tensor Processing Units (TPU) which Google uses power its Google Compute Engine. The company's latest large language-translation models take a full day to train on 32 of the current top-end GPUs.

These maneuvers could complicate Google's relationship with Nvidia, which called out the cloud computing firm as a customer in its annual report.

At the same time, chip suppliers are pouring billions of dollars to stay ahead of the machine learning demands of cloud companies like Google and Microsoft, which has also devised custom chips for data centers.

Little was known about tensor processing units made by Google especially for machine learning until last month, when 75 co-authors published a paper about the chip. The four-processor board is described as a machine-learning "accelerator" that can be accessed from the cloud and used to train machine-learning models.

Google isn't the only company investing in hardware to help with machine learning.

So how do Google's new Cloud TPUs perform?

According to Google, the chip can achieve 180 teraflops of floating-point performance, which is six times more than Nvidia's latest Tesla V100 accelerator for FP16 half-precision computation. A TPU pod contains 64 TPUs and provides up to 11.5 petaflops. "While one eighth of a TPU pod can do the job in an afternoon". Training, on the other hand, is how an AI algorithm is developed, and that takes exceptional resources.

That means that you can build a machine learning model - for example, to correctly identify an object in a photo is a tree, a auto or a cat. This has manifested itself in a number of different consumer products, like Google Translate's near-instantaneous ability to turn an English sentence into its Mandarin counterpart or AlphaGo's ability to play the deep, chess-like board game with superhuman proficiency. These training systems, in the more general sense, improve AI software through massive number crunching. "You can run inference on a single chip, but for training, you need to think more holistically".

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.

These newer processing units are capable of doing both inference and training, and researchers can deploy more versatile AI experiments at a faster rate than before just as long as the software is built using TensorFlow.

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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