Google’s custom Tensor Processing Units (TPUs) are now available in beta, offering developers access to the company’s powerful machine learning accelerators. These TPUs were initially designed for use with Google’s popular TensorFlow framework and promise to significantly speed up specific machine learning workflows compared to standard GPUs.
The announcement of Cloud TPUs was first made at Google’s I/O developer conference in 2017, where the company revealed that it would offer access to a limited number of developers and researchers. Each Cloud TPU features four custom ASICs with 64 GB of high-bandwidth memory and delivers peak performance of up to 180 teraflops.
Developers using TensorFlow do not need to make any significant changes to their code in order to use the Cloud TPU service. However, at present, access to Cloud TPUs is not a simple click of a button - developers have to request a quota and describe what they want to achieve with the service before gaining entry.
Currently, usage of Cloud TPUs is billed at \(6.50 per hour for each TPU, offering a performance advantage over standard Tesla P100 GPUs in the U.S., which cost \)1.46 per hour but offer only 21 teraflops of FP16 performance.
Google’s reputation in machine learning is expected to drive an influx of new users towards Cloud TPUs. However, what may be just as significant is that this offering helps Google differentiate its cloud computing service from compe*****s such as AWS and Azure. With the combination of TensorFlow and TPUs, Google now offers a unique service that others will struggle to match in the short term.
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