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What are tensor processing units and what is their role in AI?

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What are tensor processing units and what is their role in AI?

Central processing units (CPUs), graphics processing units (GPUs), and data processing units (DPUs) are all well-known in the computing world. GPUs, in particular, have gained prominence with the rise of artificial intelligence (AI).

Tensor processing units (TPUs), a creation by Google, are specialized processing units designed for handling the high-dimensional data used in AI operations. They are only available through Google’s cloud services.

So, what sets TPUs apart and why might you need them? Let’s explore their unique characteristics compared to other processors.

CPUs, the core of computing, handle a wide range of functions and orchestrate events within a system. However, there has always been a need to offload heavy processing tasks to more specialized chips.

What use are TPUs when GPUs exist?

Graphics processing units (GPUs) were originally developed for handling graphics processing in gaming but have found applications in AI due to their ability to handle matrix operations efficiently. While GPUs can handle matrix operations, TPUs are more customized for high-dimensional data processing.

Nvidia has been a prominent player in providing GPUs for AI applications, but other vendors also offer GPU solutions.

What do DPUs do?

Data processing units (DPUs) are deployed in servers to handle tasks like data transfer, data reduction, security, and analytics. By offloading these tasks from CPUs, DPUs bring specialization to specific operations, allowing CPUs to focus on general functions and orchestration.

DPUs are available from various vendors including Intel, Nvidia, Marvell, and AMD, as well as through cloud services like Amazon Web Services with its Nitro cards.

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What’s special about TPUs?

Tensor processing units (TPUs) are custom-built for handling high-dimensional data in AI processing. They utilize ASIC chips specifically designed for calculations involving high-dimensional numbers, with matrix-multiply units (MXUs) as a key component.

Google’s TPUs are optimized for use with the TensorFlow AI software framework and offer high computational performance for AI tasks. While TPUs excel in certain AI operations, GPUs remain a viable option for building AI systems in-house.

Google’s TPUs support frameworks like PyTorch and Jax, enabling tasks such as image classification, language modeling, and more.

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