The buzz around artificial intelligence keeps getting louder, and at the center of it all are AI chips — specifically AI accelerators, which are built to handle the massive data loads and complex computations required in modern AI systems. Common types include GPUs, TPUs and other purpose-built chips that are optimized for AI workloads.
AI Accelerator Definition
AI accelerators are pieces of hardware — usually specialized computer chips — that are built with the intention of speeding up AI workflows. They use a technique called parallel processing to perform multiple calculations at once, making systems faster and more computationally efficient.
AI accelerators are what enable large language models to generate coherent text, and autonomous vehicles make split-second driving decisions. While they’re certainly not the only component of AI development, they’re a critical piece, turning vast amounts of data into real-time intelligence.
But what exactly makes these chips so special? And why are global superpowers like the United States and China scrambling to control their supply? Here’s a closer look at how AI accelerators work, what sets them apart from more traditional chips and how they could take artificial intelligence to the next level, promising even more advanced applications.
What Is an AI Accelerator?
An AI accelerator is any piece of hardware — most often specialized computer chips — designed specifically to speed up AI workflows. They can be separate hardware added to a system, or components built directly into the computer processing unit (CPU), the brain of a computer that controls its main functions.
AI accelerators process massive volumes of data efficiently using a technique called parallel processing, which breaks down computational tasks into smaller ones that can be executed simultaneously across multiple CPUs. This makes it especially well-suited for powering data-intensive AI workloads, such as training a machine learning or deep learning system run on neural networks.
How Do AI Accelerators Work?
Parallel processing is only one part of the puzzle. To understand how AI accelerators meet the demands of modern AI workloads, it’s important to take a closer look at their hardware, as well as some other key features that drive their performance.
Hardware Architecture
AI accelerators are built from silicon or another semiconductor material. They also consist of a transistor linked to an electronic circuit, which sends electrical currents throughout the material. These currents are turned on and off to produce signals that a digital device reads. In AI accelerators designed to handle more complex calculations, electrical signals can be turned on and off billions of times a second.
Parallel Processing
Parallel processing occurs when a computational task is broken up and distributed across multiple CPUs, which solve these smaller tasks simultaneously. This method is ideal for training AI models, which may need to process up to thousands of data points at once. AI accelerators are often equipped with multiple processing units, so they can handle these large volumes of data without experiencing latency or other performance issues.
On the other hand, CPUs perform tasks sequentially, or one at a time. Adding AI accelerators relieves CPUs of some of these tasks and speeds up computations, improving the system’s capabilities and efficiency.
Reduced Precision Arithmetic
Many AI models don’t need high precision to achieve accurate outcomes. That’s why AI accelerators come with a feature known as “reduced precision arithmetic,” which uses fewer computer bits to complete calculations. This allows a system to solve a computation quickly while conserving energy and maintaining accuracy.
Memory Hierarchy
How fast a calculation can happen directly depends on how smoothly data moves through a system. Using what’s called a “memory hierarchy,” AI accelerators arrange different types of memory — such as cache memory, random access memory and high-bandwidth memory — in a way that minimizes data bottlenecks and ensures a system is running fast enough to keep up with complex AI workloads.
Types of AI Accelerators
There are several main types of AI accelerators that offer both specialized capabilities and broader approaches covering deep learning, generative AI and more.
Graphics Processing Unit (GPU)
A graphics processing unit (GPU) is a computer chip that was initially designed to create computer graphics, especially for video games. Because it’s also capable of parallel processing, it’s been used to train AI models. Linking many GPUs to an AI system boosts its processing power, supporting applications like image processing, computer vision and natural language processing.
Tensor Processing Unit (TPU)
A tensor processing unit (TPU) is a specialized computer chip developed by Google that accelerates machine learning workloads. Designed to be used in tandem with Google’s TensorFlow framework, TPUs excel at training AI models and equipping them with AI inference — the ability to glean insights from new or unfamiliar data. This makes TPUs a popular option for training neural networks, in particular.
Neural Processing Unit (NPU)
A neural processing unit (NPU) is made explicitly for handling deep learning and neural network workloads. NPUs are meant to process massive volumes of data faster than their counterparts while offering high bandwidth. As a result, they are used in image recognition and voice recognition tasks. Chatbots like ChatGPT also rely on NPUs to function.
Application-Specific Integrated Circuit (ASIC)
An application-specific integrated circuit (ASIC) is a chip that’s intended to handle a specific task or function. Because of their specialized approach, ASICs can become more efficient at performing their dedicated tasks over time than other chips. However, they can’t be reprogrammed, so they do not have the flexibility seen in more general-purpose chips.
Field-Programmable Gate Array (FPGA)
A field-programmable gate array (FPGA) also specializes in certain tasks, but it’s much more flexible than an ASIC. FPGAs consist of hardware that can be reprogrammed to suit the needs of different tasks, providing a degree of customization not seen in some other chips. FPGAs are commonly used in edge AI devices, data centers and Internet of Things (IoT) systems, among other applications.
AI Accelerator Use Cases
A wide range of industries have come to rely on AI accelerators to run their advanced AI applications.
Automotive
In self-driving cars, AI accelerators are used to handle the large volumes of real-time data gathered by cameras and sensors. With the ability to process information directly from its environment, an autonomous vehicle can make instant decisions based on pedestrian activities, traffic patterns, weather conditions and other factors.
Edge Computing
Edge computing applications rely on AI accelerators to process data directly from sources like IoT devices, even if they aren’t connected to the internet. By supporting tools in closer proximity to data sources, AI accelerators can reduce latency and enable edge AI applications to conserve energy.
Robotics
AI accelerators enhance capabilities like image processing and computer vision, enabling robots to better sort items in warehouses, identify and respond to human needs in hospitality situations and more. AI accelerators are also vital to advances in machine learning, further linking them to the future of robots.
Healthcare
Healthcare professionals can use AI accelerators to power medical imaging tools, ideally leading to faster and more accurate diagnoses. They can aid in other data-related problems as well, such as assessing historical data to determine personalized treatments for patients and evaluating hospital data to create more efficient workflows.
Finance
Financial institutions depend on AI accelerators to analyze vast amounts of financial data, assessing potential risks and detecting fraudulent activity. The ability of AI accelerators to quickly process large data sets makes them essential for algorithmic trading as well, enabling algorithms to execute decisions based on real-time data.
Benefits of AI Accelerators
AI accelerators can elevate the performance of AI-driven technologies in several ways, helping businesses develop more efficient operations.
Faster Performance
AI accelerators may possess hundreds or even thousands of cores, so they can perform demanding calculations much faster than normal CPUs. This reduces latency, removing delays as AI accelerators complete tasks.
Greater Energy Efficiency
AI accelerators are equipped with fewer nodes than other hardware and are designed to limit data movement as much as possible. These features cut down on energy usage, making AI accelerators crucial for conserving resources in data centers and edge devices.
Better Model Performance
During training, large language models must be able to handle lots of data, including complex, multimodal data. AI accelerators allow these models to process this data at high speeds, improving their performance and accuracy across different products. For example, LLMs can use enhanced capabilities like natural language generation to power chatbots.
Improved Scalability
AI accelerators come with the computational power and memory capacity to handle a variety of AI-related challenges. When integrated into existing infrastructure, AI accelerators let companies scale their AI operations over time to address more demanding tasks.
Lower Long-Term Costs
Although AI accelerators require a hefty initial investment, they offer greater savings in the long run. AI accelerators can process data using fewer computational resources compared to other hardware, helping businesses reduce costs.
Limitations of AI Accelerators
Despite delivering enhanced AI tools and better business outcomes, AI accelerators come with a few downsides that need to be taken into account.
High Energy Demands
Although they are more efficient than CPUs, AI accelerators still consume sizable amounts of electricity to complete calculations. They can strain a data center’s resources and cooling systems, canceling out many of the efficiencies they may provide.
High Initial Costs
AI accelerators can lower long-term costs, but purchasing one still comes with a hefty price tag, costing hundreds or even thousands of dollars each. Businesses may not have the resources to afford AI accelerators, especially if they need to invest in updating their current infrastructure first.
Integration Challenges
Before deploying AI accelerators, organizations need to make sure their tech stacks are compatible with the hardware. Companies may not even have the skilled personnel needed to upgrade and maintain these systems as well.
Rapid AI Innovation
The progression of AI accelerators hasn’t kept pace with the innovations seen in AI models and applications. As a result, AI accelerators may not integrate well with some of the more recent AI tools on the market.
Ethical Concerns
Because AI accelerators deal with massive amounts of data, they could play a part in potentially exposing sensitive information. Businesses need to make sure they comply with data privacy regulations. As AI progresses, there are also fears that it could displace workers, raising questions around how technologies like AI accelerators should be used.
Frequently Asked Questions
What does an AI accelerator do?
AI accelerators speed up AI workloads using a technique called parallel processing. This involves breaking a task down into smaller steps that are handled simultaneously.
What is the difference between a GPU and an AI accelerator?
A graphics processing unit (GPU) is a type of AI accelerator that was initially created to render computer graphics, particularly for video games. Since GPUs can perform parallel processing, they are now used to train AI models as well. Meanwhile, an AI accelerator refers to any type of computer chip that speeds up AI workflows, including GPUs.
What is the fastest AI accelerator?
As of now, the Wafer Scale Engine 3 (WSE-3) is considered the fastest AI chip in the world. It was built by Cerebras Systems and possesses 4 trillion transistors, doubling the performance of the WSE-2 — the previous record-holder for the world’s fastest chip.
