Google has spent a decade building servers that can handle billions of web searches a day. The company is now developing chips to deliver the smartest results.
At its annual developer conference on Wednesday, Alphabet introduced the second generation of Google’s tensor processing unit (TPU), which is designed for artificial intelligence (AI) workloads. Google unveiled the first version in 2016 and said it had started work on the “stealthy project” a few years earlier.
The upgraded version is the latest indication that Google doesn’t want to depend on other companies for core computing infrastructure. It’s potentially troubling news for Nvidia, whose graphics processing units (GPUs) have been used by Google for intensive machine learning applications. Nvidia even named Google Cloud as a notable customer in its latest annual report.
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 the new data. Over the past five years, GPUs have become a standard for the training stage of deep learning, which can be used for image recognition, speech recognition and other applications.
While the original TPU was only meant for the inference stage of deep learning, the new version can handle training as well.
“I would expect us to use these TPUs more and more for our training needs, making our experiment cycle faster and more rapid,” said Jeff Dean, a senior fellow and head of the Google Brain research team, in a media briefing on Tuesday. The company is sill using “GPUs internally for some kinds of models,” he said.