The AI industry is advancing at a rapid pace, with breakthroughs in ML and generative AI in the news almost every day. As AI technology develops, AI chips have become essential in creating AI solutions at scale. For example, delivering a modern AI application like facial recognition or large-scale data analysis using a traditional CPU—or even an AI chip from a few years ago—would cost exponentially more. Modern AI chips are superior to their predecessors in 4 critical ways: they're faster, higher performing, more flexible and more efficient.

AI chips use a different, faster computing method than previous generations of chips. Parallel processing, also known as parallel computing, is the process of dividing large, complex problems or tasks into smaller, simpler ones. While older chips use a process called sequential processing (moving from one calculation to the next), AI chips perform thousands, millions—even billions—of calculations at once. This capability allows AI chips to tackle large, complex problems by dividing them up into smaller ones and solving them at the same time, exponentially increasing their speed. 

AI chips are much more customizable than their counterparts and can be built for a specific AI function or training model. ASIC AI chips, for example, are extremely small and highly programmable and have been used in a wide range of applications—from cell phones to defense satellites. Unlike traditional CPUs, AI chips are built to meet the requirements and compute demands of typical AI tasks, a feature that has helped drive rapid advancements and innovations in the AI industry.

Modern AI chips require less energy than previous generations. This is largely due to improvements in chip technology that allow AI chips to distribute their tasks more efficiently than older chips. Modern chip features like low-precision arithmetic enable AI chips to solve problems with fewer transistors and, therefore, lesser energy consumption. These eco-friendly improvements can help lower the carbon footprint of resource-intensive operations like data centers.  

Since AI chips are purpose-built, often with a highly specific task in mind, they deliver more accurate results when performing core tasks like natural language processing (NLP) or data analysis. This level of precision is increasingly necessary as AI technology is applied in areas where speed and accuracy are critical, like medicine.

While there are many qualities that make AI chips crucial to the advancement of AI technology, there are also challenges facing the widespread adoption of these powerful pieces of hardware:

According to The Economist¹, chipmakers on the island of Taiwan produce over 60% of the world’s semiconductors and more than 90% of its most advanced chips. Unfortunately, critical shortages and a fragile geopolitical situation are constraining growth. Nvidia, the world’s largest AI hardware and software company, relies almost exclusively on Taiwan Semiconductor Manufacturing Corporation (TSMC) for its most advanced AI chips. Taiwan’s struggle to remain independent from China is ongoing, and some analysts have speculated that a Chinese invasion of the island might shut down TSMC’s ability to make AI chips altogether.

As developers build larger, more powerful AI models, computational demands are increasing faster than advancements in AI chip design. Improvements in AI hardware are coming, with companies exploring areas like in-memory computing and AI-algorithm-enhanced performance and fabrication to increase chip algorithmic efficiency, but they aren’t moving as fast as the increases in computational demand of AI applications. 

As performance demands increase, AI chips are increasing in size and requiring greater amounts of energy to function. Modern, advanced AI chips need hundreds of watts of power per chip, an amount of energy that is difficult to direct into small spaces. Significant advancements in power delivery network (PDN) architecture are needed to power AI chips or their performance will be affected.

The term AI chip refers to an integrated circuit unit that is built out of a semiconductor (usually silicon) and transistors. Transistors are semiconducting materials that are connected to an electronic circuit. When an electrical current is sent through the circuit and turned on and off, it makes a signal that can be read by a digital device as a one or a zero. In modern devices, such as AI chips, the on and off signals switch billions of times a second, enabling circuits to solve complex computations using binary code to represent different types of information and data.

Chips can have different functions; for example, memory chips typically store and retrieve data while logic chips perform complex operations that enable the processing of data. AI chips are logic chips, processing the large volumes of data needed for AI workloads. Their transistors are typically smaller and more efficient than those in standard chips, giving them faster processing capabilities and smaller energy footprints.

Parallel processing

Perhaps no other feature of AI chips is more crucial to AI workloads than the parallel processing feature that accelerates the solving of complex learning algorithms. Unlike general-purpose chips without parallel processing capabilities, AI chips can perform many computations at once, enabling them to complete tasks in a few minutes or seconds that would take standard chips much longer. Because of the number and complexity of computations involved in the training of AI models, AI chips’ parallel processing capabilities are crucial to the technology’s effectiveness and scalability.

There are several different kinds of AI chips that vary in both design and purpose:

GPUs

Graphics processing units (GPUs) are electronic circuits designed to speed computer graphics and image processing on various devices, including video cards, system boards, mobile phones and personal computers (PCs). Although they were initially built for graphics purposes, GPU chips have become indispensable in the training of AI models due to their parallel processing abilities. Developers typically connect multiple GPUs to the same AI system so they can benefit from even greater processing power.

FPGAs

Field programmable gate arrays (FPGAs) are bespoke, programmable AI chips that require specialized reprogramming knowledge. Unlike other AI chips, which are often purpose-built for a specific application, FPGAs have a unique design that features a series of interconnected and configurable logic blocks. FPGAs are reprogrammable on a hardware level, enabling a higher level of customization. 

NPUs

Neural processing units (NPUs) are AI chips built specifically for deep learning and neural networks and the large volumes of data these workloads require. NPUs can process large amounts of data faster than other chips and perform various AI tasks such as image recognition and NLP capabilities for popular applications like ChatGPT.

ASICs

Application-specific integrated circuits (ASICs) are chips custom-built for AI applications and cannot be reprogrammed like FPGAs. However, since they are constructed with a singular purpose in mind, often the acceleration of AI workloads, they typically outperform their more general counterparts.