Tensor Processing Units (TPUs) represent a leap forward in accelerating machine learning workflows, offering specialized architecture that optimizes the processing of tensor operations, which are foundational to many AI algorithms, particularly deep learning. Developed by Google, TPUs are engineered to execute operations in parallel and at high speeds, dramatically reducing the time required for training and inference phases of deep learning models. Unlike general-purpose CPUs or even GPUs (Graphics Processing Units), which can handle a broad range of computations, TPUs are specifically optimized for the high-volume, low-precision arithmetic that is often sufficient and preferable for machine learning tasks. This focus allows TPUs to achieve higher throughput and efficiency for tasks such as neural network training and execution, translating into faster model development and deployment cycles.
The concept of TPUs was first introduced by Google in 2016, with the aim of enhancing the speed and efficiency of its machine learning systems. Google revealed that it had been using TPUs internally for over a year to power products such as Google Search and Street View, marking a significant milestone in the adoption of specialized AI hardware in commercial applications.
Google's TPU development team has played a pivotal role in the evolution and deployment of TPUs. While specific individuals are not often highlighted in Google's announcements, the collective effort of Google's engineers and researchers in developing and integrating TPUs into their infrastructure has been critical for advancing the state of AI hardware.