TPU, GPU, CPU: Which Is Better for Deep Learning?

Tensor Processing Unit (TPU)

Tensor Processing Units (TPUs) are specialized hardware for machine learning and deep learning applications. Developed by Google, TPUs have been in use by Google since 2015 and were made available to the public in 2018. Built using TensorFlow, a deep learning platform developed by the Google Brain team, TPUs leverage Google’s expertise in machine learning to deliver performance benefits. TensorFlow provides advanced tools and libraries that allow users to easily build and run machine learning applications.

TPUs are particularly well-suited for deep learning models with large and complex neural networks. They can significantly accelerate the training of these models, making it possible to complete training in hours rather than weeks. While CPUs and GPUs can handle machine learning and deep learning tasks to a certain extent, these tasks are too demanding for a GPU and even more so for a CPU. TPUs, however, take things to a whole new level. As part of the memory subsystem architecture, TPUs manage the data dimensions of N*N data units, allowing them to perform more than one hundred thousand operations per cycle, far more than a CPU or GPU.

AIEX provides a complete computer vision platform that supports different GPUs, which means you don’t have to worry about choosing a processor or coding your models. You can simply select your model and train it on the intended processor using your prepared dataset.