Apart from the introduction of TPU, this article on MiniTool Website will give you a comparison of TPU and GPU for a better understanding of TPU. TPU can also be named as Tensor Processing Unit and more information will be disclosed in this article if you are interested.

What Is a TPU?

Talking about TPUs, some of you might think about CPUs (Central Processing Units); it is known that the CPU is used as a central part of a computer, which is flexible enough to perform the next instructions from application software. However, TPU just runs in a different way and for disparate purposes.

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What is a TPU? TPU is an abbreviation for Tensor Processing Unit. Different from other processing units, TPU is used as an AI accelerator application-specific integrated circuit (ASIC), which is created for neural network machine learning.

Compared to a graphics processing unit, it is designed for high-volume and low-precision calculations, which have more input/output operations per joule and no hardware for texture mapping.

Different from the CPU which performs sequentially, GPU performs the computation in a parallel manner with thousands of ALU (Arithmetic Logic Units) in a single processor, so that you can perform thousands of multiplications simultaneously.

Besides, different types of processors are suited for different types of machine learning models. For TPUs, CNN (Convolutional Neural Network) is more suitable for analyzing visual imagery.

When to Use TPUs?

TPUs are developed by Google and have been part of Google programs as new processors, such as Google Street View text processing, Google Photos, and Google Search Results. Besides, the use of TPUs also takes effect in Alpha Go – an artificial intelligence, the winner who defeats a professional human Go player.

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TPUs can be applied to multiple situations, such as fraud detection, computer vision, natural language processing, self-driving cars, vocal AI, agriculture, virtual assistants, and e-commerce, for its special advantages:

  1. The cloud resources of TPU can accelerate the performance of linear algebra computation, thus improving the performance of machine applications.
  2. When you train large and complex neural network models, it minimizes the time it takes to reach accuracy.
  3. Previously designed models require only a few hours of training on the TPU.

Since TPUs are highly specialized hardware for deep learning, you can use the TPU for specific scenarios that will yield the best results when training the AI. The following scenarios can be your reference.

  • The model that relies heavily on the operations of matrix computation.
  • AI analyses models that take weeks or months to complete with large numbers of data points.
  • Instances without custom Tensor Flow models inside the main training loop.
  • Larger and very large models with very large effective batch sizes.

Of course, if TPUs have specific situations to maximize their effects, there are some scenarios not suited to using TPUs.

  • A workload that is not dominated by matrix multiplication
  • Models limited by the available data points
  • Workloads that access memory in a sparse manner
  • Rapid prototyping with maximum flexibility
  • Models reliant on custom TensorFlow operations written in C++


According to the above contents, you have understood some basic information about TPUs and in the first place, we have mentioned CPUs and then for further reading, this article will show you something about TPU vs GPU.

Pros and Cons of TPU vs GPU

Pros and Cons of TPU

TPUs have many advantages; for example, TPUs are generally used for accelerating machine learning applications and scaling applications quickly. TPUs are designed with the benefit of Google’s deep experience and leadership in machine learning.

With the help of TPUs, you can effectively manage machine learning workloads and the whole process will start with well-optimized, open-source reference models.

The major downside of TPUs is their higher cost than GPUs but their advantages make them worth trying.

Pros and Cons of GPU

A graphics card is a device that renders images and displays output on a monitor screen. It plays an important role in playing games; using a graphics card, users can enjoy playing games with decent frame rates without any problem of lagging.

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Besides, it can increase the system performance to a greater extent and enhance your video experience.

As for its cons, graphics card consumes more power and affects the computer speed.

Bottom Line:

TPU (Tensor Processing Unit), as an AI accelerator, attracts people’s interest, especially those who are crazy about AI training. You can find some useful information in this article and have a better understanding of that.

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