Google’s TensorFlow platform allows its users to train an AI by providing tools and resources for machine learning. For a long time, AI engineers have used traditional CPUs and GPUs to train AI. Although these processors can handle various machine learning processes, they are still general-purpose hardware used for various everyday tasks.

To speed up AI training, Google developed an Application Specific Integrated Circuit (ASIC) known as a Tensor Processing Unit (TPU). But, what is a Tensor Processing Unit, and how do they speed up AI programming?

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What Are Tensor Processing Units (TPU)?

Tensor Processing Units are Google’s ASIC for machine learning. TPUs are specifically used for deep learning to solve complex matrix and vector operations. TPUs are streamlined to solve matrix and vector operations at ultra-high speeds but must be paired with a CPU to give and execute instructions. TPUs may only be used with Google’s TensorFlow or TensorFlow Lite platform, whether through cloud computing or its lite version on local hardware.

Applications for TPUs

Google has used TPUs since 2015. They have also confirmed the use of these new processors for Google Street View text processing, Google Photos, and Google Search Results (Rank Brain), as well as to create an AI known as AlphaGo, which has beaten top Go players and the AlphaZero system that won against leading programs in Chess, Go, and Shogi.

TPUs can be used in various deep learning applications such as fraud detection, computer vision, natural language processing, self-driving cars, vocal AI, agriculture, virtual assistants, stock trading, e-commerce, and various social predictions.

When to Use TPUs

Since TPUs are high specialized hardware for deep learning, it loses a lot of other functions you would typically expect from a general-purpose processor like a CPU. With this in mind, there are specific scenarios where using TPUs will yield the best result when training AI.

The best time to use a TPU is for operations where models rely heavily on matrix computations, like recommendation systems for search engines. TPUs also yield great results for models where the AI analyzes massive amounts of data points that will take multiple weeks or months to complete. AI engineers use TPUs for instances without custom TensorFlow models and have to start from scratch.

When Not to Use TPUs

As stated earlier, the optimization of TPUs causes these types of processors to only work on specific workload operations. Therefore, there are instances where opting to use a traditional CPU and GPU will yield faster results. These instances include:

• Rapid prototyping with maximum flexibility
• Models limited by the available data points
• Models that are simple and can be trained quickly
• Models too onerous to change
• Models reliant on custom TensorFlow operations written in C++

TPU Versions and Specifications