The global AI chip market value is expected to grow from $6,638 million in 2018 to $91,185 million by 2025.  

There is little to no question that the pandemic catapulted artificial intelligence into our daily lives—from smartphones to automatic cars. This, in turn, increased the number of consumers and organizations incorporating devices and systems that pin hopes on artificial intelligence, ML and other modern technological advancements. As AI becomes more and more ubiquitous, there is an increasing demand for AI hardware and processors.  

With almost every business sector in the world poised to foster artificial intelligence in its crucial business activities, the demand for AI chips is on an upward trajectory. In this blog, you will understand what is an AI chip and how it could power AI to the next level.  

What is an AI chip? 

An AI chip (also called AI hardware or AI accelerator) is an integrated circuit that is specially designed to run machine learning workloads, via programming frameworks such as Google’s TensorFlow and Facebook’s PyTorch. These chips are specially built for applications that use ANN (artificial neural networks, a machine learning approach inspired by human brain). A typical AI chip consists of FPGAs (Field-Programmable Gate Arrays), GPUs (Graphics Processing Units) and ASICs (Application-Specific Integrated Circuits).  

Is AI chip really all that different from existing CPUs? 

The shortest answer is yes. AI chips are certainly unique and built to work the way our human brains would process and execute tasks that are highly complex and dynamic. Unlike general-purpose chips, made-for-AI chips come with AI-optimized features which can dramatically accelerate calculations and computing tasks required by AI algorithms. One of the reasons why AI chips are way better and faster than ordinary silicon chips, which are designed to execute tasks in sequences, is that they can execute an enormous number of calculations in parallel. Just like human brains, AI chips are massively parallel by processing multiple streams of information simultaneously. 

Could AI chips bridge the semiconductor production gap? 

In less than five years, we will see a transformation of the global chip supply chain that will better facilitate the use of capacity, and we believe silicon remastering will be the next critical technology.  

Aart de Geuss, the co-founder, chairman and co-CEO of Synopsys Inc  

It is no secret that there is a global chip shortage. With demand comes shortage. Ever since the pandemic when the demand for semiconductors saw an unprecedented growth, there has been a serious shortage for chips in the market, affecting more than 169 countries, especially those of the most automobile manufacturers. However, there seems to be a reasonable solution to it, and it is AI. But how?  

Companies like Synopsys and Nvidia say they have an answer for it. Silicon Remastering. With the help of AI algorithms, millions of semiconductors can be redesigned automatically in a way that can achieve new capabilities to handle modern, complicated computing tasks. This way the remastered chips could help achieve months or years of work in just weeks. 

Cutting-edge AI applications require cutting-edge AI chips 

As most AI applications are cloud-based, it takes a massive amount of energy and time to move data over the long distances between multiple servers and devices. With AI chips, one can completely eliminate the need to rely on the cloud, as these tiny chips can run AI programs locally, on the devices. Today, semiconductors are, in essence, the basic building block for any AI application or device. Though semiconductors have come a long way in the past few decades, building AI applications needs chips that are built with specific architecture to support deep learning-based applications.

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