Tensor Processing Unit (TPU)
WhatIs, Monday, July 15th, 2024
A tensor processing unit (TPU) is an application-specific integrated circuit (ASIC) specifically designed to accelerate high-volume mathematical and logical processing tasks typically involved with machine learning (ML) workloads.
Google designed the tensor ASIC, using TPUs for in-house neural network ML projects as early as 2015 with Google's custom TensorFlow software. Google released the TPU for third-party use in 2018. Today, the evolving TPU chips and TensorFlow software framework are ML infrastructure mainstays, including the Google Cloud Platform (GCP).
How do TPUs work?
TPUs provide a limited number of features and functionalities that are directly useful to ML and artificial intelligence (AI) tasks but are not necessarily useful for everyday general computing. ML models and the AI platforms that use them, such as deep learning and neural networks, require extensive mathematical processing. While it's possible to execute these tasks in ordinary central processing units (CPUs) or more advanced graphics processing units (GPUs), neither is optimized for such tasks.