TPU (Tensor Processing Unit) is a type of hardware accelerator designed specifically for Machine Learning (ML) workloads. It is a Google-developed ASIC (Application Specific Integrated Circuit) designed to support the TensorFlow framework, which powers a majority of Google’s deep learning applications.
TPUs are built specifically for the purpose of accelerating neural network computations, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other deep learning networks. They are designed to run sophisticated and complex ML models that require a lot of processing power, while freeing up computing resources on the CPU.
To understand what TPU types are, it is important to understand how TPUs are used in ML. Machine learning algorithms require massive amounts of computation to process data and generate accurate results. More complex models require greater resources for running, which further slows down the training process. This is where TPUs come in – they provide significant acceleration to the ML computations by offloading the computations to specialized hardware that is optimized for the type of calculations involved in these algorithms.
There are a few different types of TPU available, each of which is designed to address different needs for ML tasks.
1. Cloud TPUs:
Cloud TPUs are hosted by Google Cloud and are available to use as part of their AI Platform. These TPUs can be used for both training and inference (i.e., using an already trained model to make a prediction). They can also be plugged into other cloud services, such as TensorFlow and PyTorch.
2. Edge TPUs:
Edge TPUs are designed for deploying machine learning models on devices operating at the edge of the network, such as mobile phones, home automation devices, and IoT devices. These are small and cost-effective chips that can run ML models on the edge without the need for cloud connectivity.
3. Cloud TPU Pods:
Cloud TPU Pods are a cluster of interconnected TPUs designed to provide even more processing power than Cloud TPUs. These Pods can include tens of thousands of individual chips to provide truly massive compute power for complex and large-scale ML workloads.
TPUs are a game-changer for Machine Learning, allowing data scientists and researchers to train models much faster and more efficiently than on traditional hardware. TPU types differ in their capabilities and applications, making them a flexible and scalable option for organizations looking to build and deploy ML models.