Alphabet Inc's Google released on Tuesday new details about the supercomputers it uses to train its artificial intelligence models, saying the systems are both faster and more power-efficient than comparable systems from Nvidia Corp .
Google has designed its own custom chip called the Tensor Processing Unit, or TPU. It uses those chips for more than 90% of the company's work on artificial intelligence training, the process of feeding data through models to make them useful at tasks such as responding to queries with human-like text or generating images.detailing how it has strung more than 4,000 of the chips together into a supercomputer using its own custom-developed optical switches to help connect individual machines.
The models must instead be split across thousands of chips, which must then work together for weeks or more to train the model. Google's PaLM model - its largest publicly disclosed language model to date - was trained by splitting it across two of the 4,000-chip supercomputers over 50 days. "Circuit switching makes it easy to route around failed components," Google Fellow Norm Jouppi and Google Distinguished Engineer David Patterson wrote in a blog post about the system. "This flexibility even allows us to change the topology of the supercomputer interconnect to accelerate the performance of an ML model."
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