Remember the bike racing game back in your school days? Life was offline then, toggling between Road Rash and Solitaire on your mammoth CRT monitor.

The computer perhaps changed more than it changed life for us since then.

And, in sync changed its brain, perhaps in an inverse dynamics. It shrank to create more room for virtual data. The quintessential Central Processing Unit – the CPU, as we call the brain of our good-old computers – was taken over by the Graphics Processing Unit (GPU) as games evolved and content diversified, until we turned more artificially intelligent, calling for the more advanced Tensor Processing Unit (TPU).

But, what is this all about? Nothing much – just a smarter brain for a wiser computing that deals with super snazzy gaming, high resolution videography, and a huge pool of data. In a nutshell, it accelerates machine learning (ML) and Artificial Intelligence (AI) workloads.

TPUs recently hit the headlines when OpenAI turned to Google to use its AI chips to power ChatGPT. OpenAI started renting Google’s TPUs, which could potentially help it lower the inference cost while also diversifying its dependence beyond leading chip maker NVIDIA and tech major Microsoft.

And, what is this inference cost? It is the cost an AI company bears for compute time, memory, and data transfer to help trained models arrive at a conclusion or make a prediction from analysing the existing set of data.

Although OpenAI later said that it was only “conducting early testing” with Google’s TPUs and had no plans to use them at scale, one cannot help asking: what are the implications of TPUs? Or, to start with, what are TPUs, how do they stand out from GPUs, and how are they going to change the AI model development process?

Before OpenAI, Anthropic announced its partnership with Google Cloud in 2023 to access its GPU and TPU clusters to train, scale, and deploy its AI systems. Even Apple used Google’s TPUs to train two of its AI models recently.

As AI gets deeper into our lives every passing day, there’s an increasing demand for compute systems across the world. Can Google’s specialised chips accelerate the AI journey?

Let’s dive deep to get into the multilinear link across data sets in the world of tensor processing.

Understanding The TPU

Google created a kind of computer chip a few years ago to help power its giant AI systems. These were designed for the complex processes that could be a key to the future of the computer industry. The internet giant said it would allow others to buy access to those chips through its cloud-computing service as it hopes to build a new business around the tensor processing units.

The tech giant started deploying these application-specific integrated circuits (ASICs) or TPUs back in 2015. Its first chip was TPU v1. Google kept upgrading the technology in tandem with the evolution in AI and ML.

The TPU, which can be referred to as an AI accelerator, is used in building agents, recommendation engines, and personalisation models in code generation, media content creation, synthetic........

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