Google Constructs Novel Chip To Make Machine Learning Faster

It all started as a stealthy project at Google several years ago to gauge what could be accomplished with their own custom accelerators for machine learning applications; and the result is a custom chip dubbed Tensor Processing Unit, or TPU, an ASIC named after the TensorFlow software which Google uses for its machine learning programs. The ASIC or application-specific integrated circuit, is specific to deep neural nets. These are networks of hardware and software that learn specific tasks by analyzing vast amounts of data.

google-tensor-processing-unit-board

This technology has been imperative for the revamp of the search engine. The accelerator chip, speeds up a specific task providing better performance per watt than existing chips for machine learning tasks. Owing to this more operations per second can be squeezed into the silicon, so that the users get more intelligent results more rapidly. Google quotes it has been running TPUs for about a year now, already powering many applications at Google such as Rank Brain, Street View and AlphaGo.

tpu-server-racks-google
Server racks with TPUs used in the AlphaGo matches with Lee Sedol

At its I/O conference on Wednesday, CEO Sundar Pichai quoted the TPUs are not going to replace CPUs and GPUs but they can surely speed up machine learning processes without consuming a lot more energy. As machine learning becomes more widely used in all types of applications, from voice recognition to language translation and data analytics, Google says its TPU could provide the equivalent gains of moving Moore's Law forward by three generations, or about seven years.

The technology giant doesn’t plan on selling any of its TPUs as of yet and the build up could just be to gain more control over cost, technical specifications and the future roadmap so that it doesn’t have to rely on third-party silicon providers like Intel and Nvidia.

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