AI Chips: An Evolution of AI System's Hardware

November 23


In this era, everything is going to be automated to some extent. As time goes on, Artificial Intelligence becomes a more advanced technology and many industries want to leverage it. Now we all have a better understanding of what AI is capable of and how many systems are working under AI. From healthcare to smartphones, everything has AI to operate seamlessly, and all AI systems require specific types of hardware such as chips to improve the performance of AI systems. In this article, we are going to cover all the buzz around AI chips and how they can be crucial for developing AI platforms.

What are AI Chips, and why do they matter?

There is no single, universally accepted definition of AI chips. As the term (AI chips) refers to, it sounds like a very knowing thing. Let's talk about chips or semiconductors. It is an important part of any electronic device which is made of thin silicon that contains millions of transistors.

AI chips are a type of hardware that is used for managing AI workloads in a system or platform. It offers significant performance advantages for specific AI workloads, such as deep learning and image recognition.

It is designed for special operations on AI systems. For instance, it helps in calculating complex computational analysis and also assists with AI technologies like deep learning.

It matters a lot because traditional chips (CPUs) are unable to provide such kinds of efficiency and accuracy in the development of AI systems. Yes, some AI operations are executed by CPUs, but with time they fail to tackle advanced AI tasks.

That's where the development of AI chips has gained significant momentum.

The emergence of AI chips has increased with the advancement of AI technology and its acceptance by many industries, which are desperate for the deployment of AI in their systems. It is necessary to perform well in terms of delivering desired outcomes in AI applications.

What's special about AI chips?

In the above paragraph, we have seen the urgency of accepting AI chips as enhanced hardware for advanced AI applications. It is very special because it has various varieties of unique chips as per the requirements.

Different kinds of AI chips work in different situations. It's a very crucial thing to know what kind of AI chips are needed for AI systems or applications. Whether it need "training", or "inference" or would it need a chip that is specialized in "parallel processing"?

Let's take a look at some important types of AI chips.

CPUs (Central Processing Units): 

They are also known as "general purpose chips". As it is very useful for operating general AI tasks. However, it's not capable of doing things in advanced AI systems.

These chips are generally high in cost. For instance, the Intel Core i9-12900HX processor. 

GPUs (Graphics Processing Units):

These types of chips are specialized in parallel processing, which is the foremost reason for considering them as a "training process" for AI algorithms.

It is better than CPUs in terms of computing performance. It is very important for training the AI model. For instance, the Geforce GTX 1630.

FPGAs (Field-Programmable Gate Arrays): 

It is used for applying trained AI algorithms to real-world data intakes, as inference. In simple words, these kinds of chips complete the process after the training process of the AI model has been done. It is much more affordable than CPUs and GPUs. Also, it doesn't require an enormous amount of instructions or cache data to perform seamlessly. For instance, the Intel Arria 10 FPGA.

ASICs (Application-Specific Integrated Circuits):

Like GPUs, which are used for training, and FPGAs, which are used for inference, it is designed for specialization in both training and inference.

They are designed for specific applications and their various features make them more efficient and aligned with specific AI tasks.

Neuromorphic Chips:

These types of chips are very new in the market and at their initial stage. It is designed to mimic the human brain and its architecture, which is inspired by the human brain and promotes its neural processing capabilities.

It holds a very bright future as it can become more energy-efficient for future AI applications. For instance, Intel Lab's Loihi 2.

TPUs (Tensor Processing Units):

It is a product of Google, which is developed and designed for neural network operations. It enables the benefit of power efficiency along with amazing acceleration for AI workloads.

CPUs, GPUs, FPGAs, and ASICs are the main types of AI chips and Neuromorphic chips are developing things. TPUs were developed by Google to handle neural networks.

All of these chips play a crucial role in the development of AI systems. All these AI chips perfectly complement each other and fulfill all the shortcomings that AI systems often lack.

Advantages of AI chips:

AI chips have some tremendous benefits that are one of the reasons for them being very special. Here are some fantastic traits that make it more convenient for AI workloads.

Improved overall performance:

AI chips or AI accelerators stand out in the performance of AI-driven systems. It is designed for operating complex nuisances, which is why it has become so convenient to organize complex issues. With specialization in operating fundamental tasks for AI algorithms, it delivers high value to AI models by providing faster training and inference for them.

Increased energy efficiency:

Due to the small transistors, which consume less energy than larger transistors, AI chips boost energy efficiency by 100x. These energy-efficient AI chips use less energy for the same amount of AI computation that is done by traditional chips.

Minimizes Latency:

It is important to reduce the time that is taken to process input and produce output for an AI model. With its rich features like real-life responses and decision-making, it has done a great job of reducing latency to provide a smooth AI system or applications.

Provides faster processing and higher bandwidth:

AI chips have great command over these two terms. Both of them are crucial for sustaining any type of AI application. When it comes to parallel processing, AI models need more bandwidth of memory to perform at an optimum speed.

Benefits every industry:

Once the AI chips settle down, they can be very useful for various industries out there. With its high-performance computing, rapid processing, and other spectacular features, it can prove to be a boon for various industries like healthcare, finance, security, and many other businesses.

There are some additional benefits that: Improve PPA (power, performance & area), are reusable, and boost the overall productivity of AI workloads.

Disadvantages of AI chips:

We all know that with AI many things are going under the umbrella of skepticism. AI chips also have some dark side effects, which is a matter of discussion.

Hard to develop:

This thing exists with every new technology. It requires a solid understanding of developing EDA (electronic design automation) and needs highly skilled experts to develop AI chips which are expensive to hire. All these things seem to be an important entry barrier to tackle for the safe future of AI chips.

Security and Bias concerns:

Anything that is related to AI technology tends to have some serious security and algorithmic bias issues. As time goes on, AI chips become more and more advanced. With this advancement, it is prone to get some cyberattacks like hacking. Without considering this security concern, developers may face some difficulties in the development of AI chips.

If any kind of algorithmic biases exist in the AI chips, they affect the overall  AI application, as the data present in biases is used to train AI models.


Ever since AI technology came into existence, it has also been under suspicion. There are limited datasets for AI training, and this should increase with time. And as always with AI, skepticism will be there that “Can machines do a better job than engineers (humans)?”

The architecture of AI chips:

Cutting-edge AI chips use specialized neural networks to conduct AI tasks. Convolutional neural networks (CNNs) and Recurrent neural networks (RNNs) are the primary sources for more efficient implementation of complex computational AI tasks.

Companies that provide AI chips:


Nvidia Corp:

This company has dominated the semiconductor industry for a long time. Because of its faster processors and a huge number of transistors, its AI chips have become world-famous for computing AI systems. The H200 (GPUs) is the most recent AI chip which was announced by Nvidia.


This giant semiconductor development company became the first AI chip company to cross $1 billion in sales. With time, it developed the Xeon platinum series, which has more memory capacity and memory bandwidth. Recently Intel unveiled its AI-equipped chipsets for PCs.

AMD (Advanced Micro Devices):

This company has also emerged as a great competitor to Nvidia and Intel. It opens up new offerings for AI chip seekers who want new options for AI chips. The MI300X was one of its latest AI chips.


Recently, this tech giant introduced its first chip for AI tasks called Maia. It is designed for powering AI workloads running on Microsoft Azure. It is also working with OpenAI.

Cerebras Systems:

This is one of the most successful startups in the field of semiconductors. It was established in 2015, and now it is ready to change the world of AI with its AI accelerators.

There are many more companies and new startups that are developing AI processors for AI workloads. Companies like Google, Amazon, and IBM, and startups like Graphcore, Hailo, and SambaNova Systems, Inc. are some significant players in developing AI chips.

Also Read: 

Stable diffusion vs mid journey: an overview

Positive effects of the industrial revolution on society

What is the look ahead with AI chips?

So, as per the research and reports, AI chips have a great future, as they are expected to reach $195 billion by 2030, growing at a CAGR of 37.4% from 2021 to 2030.

According to AI experts, AI chips will be very beneficial for handling complex AI workloads to develop AI applications. The future looks good and is full of opportunities for new technologies.

But before we reach the full potential of AI chips, it's important to address all kinds of challenges that it has.

Give us your opinion on AI chips and their future.

Thanks for reading!

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