What is a TPU?

A tensor processing unit (TPU) is a specialized integrated circuit developed by Google for neural network machine learning. The TPU is a critical component of Google’s AI Supercomputer which enables the company to develop large language models, that are spurring up the current AI revolution.

Understanding TPUs

Tensor processing units – also known as TensorFlow processing units – are machine learning (ML) accelerators in the form of specialized integrated circuits. TPUs were created by Google to handle the neural network processing of TensorFlow – the company’s free and open-source software library for ML and AI purposes. 

More specifically, Google introduced TPUs in 2016 to deal with matrix multiplication operations in neural network training. They can be accessed in two different forms. The first, Cloud TPU, is offered as part of the Google Cloud Platform and hosted in the company’s data centers.

The second, Edge TPU, followed in 2018 and forms part of a custom development kit that is used to build specific applications. Edge TPU is an application-specific integrated chip (ASIC) created to run ML models in edge computing contexts, and it was made available to developers with products within the Coral brand in January 2019.

The key components of a TPU

Some of the components (and indeed vocabulary) associated with a TPU include:

  • Tensors – fundamental units of a neural network that store data (such as the weights of a node) in a row and column format. Tensors are used to perform basic mathematical calculations like addition and matrix multiplication.
  • FLOPs – floating-point operations per second (FLOPs) are used to measure the speed of the computer operation. In Google TPUs, this unit of measurement is called brain floating-point format – or bfloat16 for short – which is placed within systolic arrays to accelerate training. A higher FLOPs range is associated with higher processing power.
  • Systolic array – the collection of sensors responsible for executing computations and distributing the results across the system. These processing elements are grouped such that they are ideal for parallel processing.

Where are TPUs used?

Tensor processing units are used in DeepMInd’s AlphaGo – a computer program that beat the world’s best human player at the Chinese board game Go. TPUs are also present in the AlphaZero program which in addition to Go has mastered the games of chess and shogi. 

TPUs have also been used to power many applications at Google itself. One example is RankBrain – the core component of Google’s algorithm which employs machine learning to determine the most relevant search results. Another is in Google Photos, where a TPU can process over 100 million photos per day. TPUs were also utilized to improve the quality and accuracy of maps of navigation within Street View.

Otherwise, tensor processing units are the most effective when models need to rely on matrix computations (such as the recommendation systems for search engines we mentioned above). They are also useful when AI needs to train new models from scratch with vast amounts of data.

Key takeaways:

  • A Tensor Processing Unit (TPU) is a specialized integrated circuit developed by Google for neural network machine learning. 
  • Google introduced TPUs in 2016 specifically to deal with matrix multiplication operations in neural network training. They can be accessed in two different forms: Cloud TPU and Edge TPU.
  • Tensor processing units are used in DeepMInd’s AlphaGo and AlphaZero game-based programs. They have also been used to power many applications at Google itself such as Google Photos, RankBrain, and Street View.

Connected Business Model Analyses


Generalized AI consists of devices or systems that can handle all sorts of tasks on their own. The extension of generalized AI eventually led to the development of Machine learning. As an extension to AI, Machine Learning (ML) analyzes a series of computer algorithms to create a program that automates actions. Without explicitly programming actions, systems can learn and improve the overall experience. It explores large sets of data to find common patterns and formulate analytical models through learning.

Deep Learning vs. Machine Learning

Machine learning is a subset of artificial intelligence where algorithms parse data, learn from experience, and make better decisions in the future. Deep learning is a subset of machine learning where numerous algorithms are structured into layers to create artificial neural networks (ANNs). These networks can solve complex problems and allow the machine to train itself to perform a task.


DevOps refers to a series of practices performed to perform automated software development processes. It is a conjugation of the term “development” and “operations” to emphasize how functions integrate across IT teams. DevOps strategies promote seamless building, testing, and deployment of products. It aims to bridge a gap between development and operations teams to streamline the development altogether.


AIOps is the application of artificial intelligence to IT operations. It has become particularly useful for modern IT management in hybridized, distributed, and dynamic environments. AIOps has become a key operational component of modern digital-based organizations, built around software and algorithms.

Machine Learning Ops

Machine Learning Ops (MLOps) describes a suite of best practices that successfully help a business run artificial intelligence. It consists of the skills, workflows, and processes to create, run, and maintain machine learning models to help various operational processes within organizations.

OpenAI Organizational Structure

OpenAI is an artificial intelligence research laboratory that transitioned into a for-profit organization in 2019. The corporate structure is organized around two entities: OpenAI, Inc., which is a single-member Delaware LLC controlled by OpenAI non-profit, And OpenAI LP, which is a capped, for-profit organization. The OpenAI LP is governed by the board of OpenAI, Inc (the foundation), which acts as a General Partner. At the same time, Limited Partners comprise employees of the LP, some of the board members, and other investors like Reid Hoffman’s charitable foundation, Khosla Ventures, and Microsoft, the leading investor in the LP.

OpenAI Business Model

OpenAI has built the foundational layer of the AI industry. With large generative models like GPT-3 and DALL-E, OpenAI offers API access to businesses that want to develop applications on top of its foundational models while being able to plug these models into their products and customize these models with proprietary data and additional AI features. On the other hand, OpenAI also released ChatGPT, developing around a freemium model. Microsoft also commercializes opener products through its commercial partnership.


OpenAI and Microsoft partnered up from a commercial standpoint. The history of the partnership started in 2016 and consolidated in 2019, with Microsoft investing a billion dollars into the partnership. It’s now taking a leap forward, with Microsoft in talks to put $10 billion into this partnership. Microsoft, through OpenAI, is developing its Azure AI Supercomputer while enhancing its Azure Enterprise Platform and integrating OpenAI’s models into its business and consumer products (GitHub, Office, Bing).

Stability AI Business Model

Stability AI is the entity behind Stable Diffusion. Stability makes money from our AI products and from providing AI consulting services to businesses. Stability AI monetizes Stable Diffusion via DreamStudio’s APIs. While it also releases it open-source for anyone to download and use. Stability AI also makes money via enterprise services, where its core development team offers the chance to enterprise customers to service, scale, and customize Stable Diffusion or other large generative models to their needs.

Stability AI Ecosystem


About The Author

Scroll to Top