For years, our computers and devices have been using CPUs and GPUs to handle almost all the activities performed by them. But day by day, things are getting more and more complex, and therefore, usual chips like GPUs and CPUs are becoming obsolete to handle new and developed technologies.
Initially, CPUs handled the majority of computing tasks, like browsing and running normal and simple everyday programs. But with the rise in technology, our devices become more advanced and AI-based, so CPUs become unfit to process heavy calculations that AI models require.
To replace CPUs came GPUs. These specialized chips are capable of doing thousands of computer processes and operations in parallel and are fit for deep learning, supporting tools like ChatGPT, and image recognition systems.
But now, as tech is advancing more and more and AI models are growing larger and more complex therefore faster performance and greater energy efficiency are required. Now, for training AI models, we require many GPUs running for weeks, using large amounts of energy in the form of electricity. So, for complex models, these GPUs are no longer efficient and effective.
Now, big techs are manufacturing their own Application-Specific Integrated Circuits with the sole purpose of running AI tasks faster, using less energy, and delivering better performance.
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What Is an ASIC?
An application specific integrated circuit (ASIC) is a type of integrated circuit that consists of many electronic circuits on a single chip. Application specific integrated circuits are custom designed to perform a particular application or function rather than for general purpose use. ASICs are used in specialized systems to perform their intended function with high performance efficiency, low power consumption, and space optimization.
Because of their ability to perform tasks with high precision and speed, they are ideal choices for applications and systems which require high performance dedicated hardware.
ASICs are built for one specific task only, and they cannot perform jobs outside their design.
So if you have an ASIC that was designed for data center encryption, it cannot be used for cryptocurrency mining because the chip simply does not have the hardware circuits needed for hashing.
On the other hand, CPUs and GPUs can perform almost any task because they are general-purpose processors. They can handle encryption, mining, graphics, operating systems, and thousands of other things because they are built to do many tasks but not with same speed, energy efficiency and precision as ASICs
Why General-Purpose Chips Can’t Keep Up
CPUs and GPUs are great at performing a variety of tasks because they are general-purpose chips. But in the modern AI era, they are becoming obsolete for high tech devices and models like GPT-5, Gemini, and other large language models.
This is because these advanced models have billions of parameters and therefore require enormous computing power to run and get trained. CPUs and GPUs can handle these tasks but they use huge amounts of electricity and generate lots of heat.
Moreover they proceed all these tasks at a slower pace and might get hanged. In short, they can perform these tasks but are not efficient and effective to handle them.
GPUs are faster because they can process many things at once, but they still waste energy when performing the exact mathematical operations that AI models depend on. Data centers running thousands of GPUs consume massive power, which drives up cost and slows down scaling.
ASICs are built only for AI tasks, especially the matrix multiplication patterns that neural networks constantly use to turn raw data into useful output. . This makes them much faster and far more energy-efficient.
How ASICs Accelerate AI
As ASICs have to perform only one specific task they are specialized for, so they are extra fast. Beside this, there are some other ways in which ASICs accelerate AI based tasks. Lets discuss them!
Matrix Multiplication
ASICs accelerate AI by doing the exact type of work that modern AI models need, without carrying any extra, unnecessary functions. Most of the AI systems rely on matrix multiplication. It is a repetitive process which is used to find patterns in texts, images, or numbers. ASICs are specialized circuits which are designed specifically for dealing with matrix multiplication. Therefore these chips perform these operations much faster than general-purpose chips
Low Latency
ASICs are fixed-function chips. Fixed-function means that these chips are built to do one specific job and nothing else. Because of this property they don’t waste time switching between instructions or extra layers of software. This reduces latency. Reduced latency means instant responses.
Power Efficiency
As these are fixed-function chips and made for one specific purpose. So, they don’t have extra layers of software or blocks that sit unused as in GPUs and CPUs. Because of this property, these chips are energy efficient. ASICs contain only the parts needed for AI, so they reduce energy usage to a high level.
Custom Memory Hierarchy
Another advantage is how ASICs handle memory. Moving data inside a chip is one of the biggest sources of energy waste. ASICs chips have a custom memory layout to store the data close to those parts of the chips that need it. This reduces movement of data and reduces energy consumption.
Scalability
ASICs are designed to scale extremely well. Many ASIC units can be connected together to form pods or clusters. These clusters work as a powerful AI engine.
When multiple ASICs operate side by side, they can share the workload efficiently. They can process huge batches of data at once and deliver much higher performance without wasting energy.
As each chip is built for one specific task therefore these clusters run AI training and inference with better speed, lower heat production and improved energy efficiency as compared to general-purpose systems. This ability to scale from a single chip to a full cluster is a main reason why ASICs are becoming essential for large AI operations.
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Why Big Tech Is Designing Their Own ASICs
Big Tech companies are investing heavily in custom ASICs because they need faster, cheaper, and more reliable hardware for the next generation of AI.
Although building and designing a custom made ASICs costs a bit higher, it becomes very cost-effective when they are used in the products which have high selling potential like smartphones, tablets, wearables, and cloud servers.
Companies also choose ASICs to avoid supply chain problems, reduce the size of their devices, and perform specialized functions that general-purpose chips cannot handle efficiently. ASICs consume less electrical power, this helps meet sustainability goals.
ASICs also help protect a company’s product from being copied. Because the chip is custom-made for that device, other companies cannot buy the same chip or easily recreate it. It is like having a special tool that only you own.
From a performance point of view, ASICs are faster than regular GPUs for the specific jobs they are built to do. This is because an ASIC can be specially made to work perfectly with a company’s own AI software. When the hardware and software are designed to complement each other, everything runs smoother and AI works faster, uses less electricity, and costs less to operate.
When companies don’t make their custom ASICs, they totally depend on vendors like NVIDIA for their GPUs which are expensive and often in short supply. So, building ASICs gives big techs a lot more control. By creating their own ASICs, companies can make sure they always have the chips they need, keep their data safer, and add special features that only they can use. This is why many big companies now have their own chips
Industry Examples of ASICs
Many top technology companies have built their own custom chip to support the specific type of AI work they rely on. Let’s have a look at those companies, their application-specific integrated circuits, and their functions.
| Company | ASIC | What It Is Used For |
| Tensor Processing Unit | Tensor Processing Unit is used to train and run large AI models in data centers | |
| Apple | Apple Neural Engine | Apple Neural Engine strengthens AI features inside iPhones and iPads |
| Amazon | Inferentia and Trainium | Inferentia and Trainium are for handling AI workloads in AWS cloud services |
| Meta | Meta Training and Inference Accelerator | Meta Training and Inference Accelerator support Meta’s internal AI models |
| Tesla | Dojo D1 | Dojo D1 chip processes camera and sensor data for self-driving cars |
FAQs about Application-Specific Integrated Circuits
1. Why can’t CPUs and GPUs handle modern AI anymore?
In the early stages of computer development, CPUs and GPUs were built to deal with simple tasks like browsing, gaming, and running apps. But now technology is advancing day by day and AI models are very complex that need to process thousands of processes at the same time. Although CPUs and GPUs can perform these tasks but at a very slow pace and with large consumption of energy. Therefore they are not effective and efficient.
2. What is an ASIC?
An ASIC is a special-purpose chip made to do one specific task extremely well. So, they perform their specified task faster, use less electricity, and don’t slow down like CPUs and GPUs.
3. Why do ASICs use less electricity than GPUs?
ASICs are designed to perform only one specific task so they don’t have additional layers or blocks like GPUs and CPUs. Because of this, they don’t waste electricity and do not heat up as much.
4. If ASICs are so powerful, why can’t they do every task?
When these chips are built, they are engineered to contain only those parts which are required to perform specific functions. Unlike GPUs and CPUs they lack additional parts to do additional activities. ASICs are powerful only for the task they are built for. If an ASIC is made for encryption, it cannot do cryptocurrency mining. ASICs can do only one task, but they do it extremely fast and with very low energy.
5. Why are big tech companies making their own chips instead of using NVIDIA GPUs?
Big tech companies are making their own chips because NVIDIA GPUs are expensive and in limited supply. By designing their own ASICs, companies get faster performance, lower energy use, and full control over their hardware. They can also add unique features that competitors can’t copy.