Recently, Machine learning, particularly Deep Neural Networks (DNN) have significantly contributed to the emergence of commercial AI applications. Thanks to the improved capacity of modern computing hardware, Deep Neural Networks (DNN) were successfully applied over a decade ago. AI chips are an advanced generation of custom-built chips for various machine learning applications.

The increasing popularity of artificial intelligence fosters the need to design and create faster and cheaper chips among many technology giants. Organizations can either buy their hardware or rent this hardware on the cloud from popular cloud service vendors. Owning hardware can be cost-effective if its utilization is high. However, it will be better for organizations to rely on cloud service providers if they can’t keep the utilization high.

What is an AI chip?

AI Chips, also known as AI Hardware, are particularly designed accelerators for Artificial Neural Networks(ANN). AI chips consist of Field-Programmable Gate Arrays(FPGAs), Graphics Processing Units(GPUs), and Application-Specific Integrated Circuits(ASICs). And they are all particularly designed for AI.

Central Processing Units(CPUs), which are general-purpose chips, can also be used for some basic AI functions. However, as AI advances and becomes widely spread, they are becoming less useful.

Similar to CPUs, AI chips acquire efficiency and speed by integrating large quantities of smaller transistors. And that’s because smaller transistors use less energy and can run faster than big transistors. However, unlike CPUs, AI chips have features that are specially designed and optimized for AI.

These special features significantly increase the expected, similar, and independent calculations demanded by AI algorithms. These calculations include calculating numbers with low accuracy in a method that successfully applies AI algorithms but decreases the quantities of transistors required for the same calculation; making use of programming languages built particularly to translate AI computer code effectively for implementation on an AI chip; implementing a substantial number of calculations in parallel instead of consecutively, etc.

There are various categories of AI chips that are useful for different functions. The most popular and commonly used are GPUs, ASICs, and FPGAs.

Why Are AI Chips Better?

The AI chips offer more usefulness, advantages, and benefits than normal chips. Check out some of the reasons why AI chips are better than normal chips

More Efficient And Effective

Chips used in lightweight devices like mobile phones are popularly called mobile chips. Normal CPUs found in desktop computers and mobile phones act like the engines that control, perform, and execute any function you want them to do.

AI chips are the future of mobile chips. And that’s because they are capable of doing far beyond the basic functions of your phone. The major work of AI chips is to perform specific AI functions more efficiently and efficiently.

Performance

Normal chips are not equipped enough or better suited to satisfy the requirements of machine learning. AI chips have extra Neural Processing Units(NPUs). And they ensure better battery life and can deliver AI performance much faster. AI chips provide power efficiency and high performance for AI applications because of their heterogeneous computing abilities. With AI chips, smartphones will be able to perform numerous functions simultaneously. And that’s because the processor will be faster and much more efficient. AI chips can also operate particular programming functions more effectively and faster than normal chips ever can.

Technology

AI chips are specially designed chips that integrate machine learning and AI technology to make devices smart enough to copy the human brain. AI chips are utilized to enhance deep learning machine functions. Machine learning is just a method to achieve AI. AI chips, on the other hand, are systems with numerous processors. And each processor has its specialized function.
Higher Bandwidth

AI chips allocate four to five times more bandwidth than normal chips. This is vital because AI applications demand significantly better bandwidth between processors to work effectively and efficiently. And that’s because of their need for parallel processing.

Faster Computation

AI applications normally demand parallel computing abilities to successfully run highly developed algorithms and training prototypes. AI chips offer better parallel processing abilities that are estimated to be more than ten times the competing power of Artificial Neural Networks(ANN) applications. This is way better compared to normal hardware at similar price ranges.

Why Do We Need GPUs In AI?

As technology evolves and more organizations are implementing Artificial intelligence operations, numerous people are turning to GPUs to speed up processes. This is particularly true for organizations using deep learning processes, which are too long to run.

GPUs are microprocessors that are particularly designed and created to perform specific functions. They provide parallel processing of functions and can be enhanced to improve performance in deep learning and artificial intelligence processes.

Check out some reasons why GPUs are better in AI

Flexibility: GPUs can perform parallel processing operations. This allows organizations to combine GPUs in groups and assign tasks across the different groups. Also, they can use GPUs individually with groups distributed to the training of independent algorithms.

Higher Memory: GPUs provide better and higher bandwidth than CPUs, up to 750GB/s compared to CPUs 50GB/s. This will significantly contribute to handling the large amount of data needed for artificial intelligence.

Numbers of Cores: GPUs have numerous cores. These cores can be grouped and combined with CPUs. This will help organizations to significantly increase their processing power.

Final Thoughts

Normal chips do not have the needed computational power to satisfy numerous artificial intelligence features than AI chips can. AI chips can sustain large-scale computing functions faster than normal chips can. AI chips are designed to perform computing functions more efficiently than normal chips.