Google has custom-designed a chip to support the company’s foray into artificial intelligence. The chip will be used to promote the company’s cloud services so that businesses and coders can tap into Google’s AI engines and build their own applications.
The new accelerator chip is called the Tensor Processing Unit (TPU) and was built using Google’s open source TensorFlow algorithms. According to a blog post by Norm Jouppi, one of the top hardware engineers at Google, the accelerators have already been in use for over a year, powering some of Google’s well-known features such as RankBrain and Street View. The neural networks are programmed to learn new things by analyzing vast amounts of data.
Google has no plans to sell these chips to other companies and won’t compete directly with other chip makers like Intel or nVidia. However, since the chips will be an integral part of the hardware behind Google’s services, Intel and nVidia will notice a direct impact, especially if other companies begin to rely on the cloud computing services that Google provides.
The TPU is tailored specifically for machine learning. The designer behind the chip, Urs Hölzle, believes that TPUs will ultimately replace GPUs. The only drawback is that the chip takes at least six months to build. In an incredibly competitive market, a lead time of six months is a big deterrent for a lot of companies.
Meanwhile, Microsoft has teamed up with Intel so they can use FPGA to power their cloud computing products. Intel has already foreseen the potential that FPGA offers and made the decision to acquire Altera early last year. Compared to a traditional GPU (or even a TPU), an FPGA offers flexibility. However, Google’s TPU was designed with just one objective in mind: speed and efficiency.
Google is not the first company to design an accelerator just for AI. Both Nervana Systems and Movidius rely on their own versions.
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