What Are the Main Hardware Components Used for AI?

Contact Us

Long a plot device in science fiction, artificial intelligence (AI) has become part of everyday reality. AI-powered chatbots and virtual personal assistants are widely available. Applications such as autonomous cars, healthcare diagnostics and robotic process automation are advancing rapidly.

These applications wouldn’t be possible without powerful computing hardware. The main hardware components include graphics processing units (GPUs), AI accelerators and other specialized chips. AI developers can access these capabilities through cloud platforms, eliminating upfront investment in advanced hardware.

Table of Contents

  • Why Powerful Hardware Is Critical for AI
  • Why GPUs Beat CPUs for AI
  • What Are AI Accelerators
  • What Are Edge AI Chips
  • Other Emerging AI Hardware Technologies
  • Hardware Used for Cloud AI Platforms
  • Conclusion

Why Powerful Hardware Is Critical for AI

Advanced AI applications use deep learning techniques to solve complex problems by mimicking the activity of the human brain. Deep learning requires the ability to analyze massive datasets — much larger than those used in machine learning. Machine learning algorithms are relatively simple and applied to structured data. Deep learning uses highly complex algorithms that are applied to unstructured data such as voice and video. These algorithms create a multilayered (“deep”) neural network model that “learns” to predict patterns in the data.

In supervised learning, the neural network processes datasets that contain the answers to the problem it’s trying to solve. Over time it learns to determine the correct answers from datasets it hasn’t processed. In unsupervised learning, the dataset does not contain the answers. The neural network learns by classifying specific characteristics within the dataset and finding commonalities.

AI remained fictional for decades because computers couldn’t handle the computation involved. The central processing units (CPUs) found in most computers are not designed for deep learning algorithms. Specialized hardware provides the computing power to perform AI tasks and process large amounts of data.

Why GPUs Beat CPUs for AI

Some of the greatest breakthroughs in AI development have come from Nvidia, a company that got its start in the video game market. Nvidia revolutionized computer gaming through the development of the first GPU in 1999. These chips perform multiple mathematical calculations simultaneously to produce cleaner, faster and smoother motion in graphics. The computational processes required for AI are similar.

In 2007, Nvidia pioneered the use of GPUs to make compute-intensive applications run faster. This brought dramatic improvements over previous methods that relied on linking multiple CPUs. NVIDIA’s CUDA is a software development environment that works with commonly used programming languages and frameworks, making it easier for developers to utilize GPU resources. Today, GPUs are the most commonly used AI hardware.

Key architectural differences make GPUs more suitable for AI than CPUs. A CPU has a few cores with lots of cache memory that can handle a few software threads at a time. A GPU has hundreds of cores that can handle thousands of threads at a time. CPUs are also optimized for sequential processing, while GPUs can execute multiple processes simultaneously.

As a result, GPUs can run some software 100 times faster than CPUs. That makes them ideal for the deep learning algorithms that power a wide range of AI applications.

What Are AI Accelerators?

AI accelerators are designed for the efficient processing of AI algorithms. GPUs are a type of AI accelerator, but chips based on application-specific integrated circuits (ASICs) can deliver greater efficiency. Examples include Google’s Tensor Processing Unit (TPU) and Cerebras’s Wafer-Scale Engine (WSE). AI accelerators use techniques such as lower-precision calculations to increase throughput. They can be combined in pod configurations to provide massive compute power for neural networks. They can also be customized for specific tasks.

Writing an algorithm that can take full advantage of multiple processor cores is extremely difficult. As a result, AI accelerators require a specialized software framework. Two open source examples are PyTorch and TensorFlow, developed by Google. These frameworks include software libraries, tools and other resources to help developers create and train deep learning models.

AI accelerators are divided into two broad categories: data center and edge. Data centers tend to use enormous, massively parallel chips such as the WSE to gain the speed and scalability they need. For edge computing applications, smaller, more energy-efficient chips are needed.

What Are the Main Hardware Components Used for AI?

What Are Edge AI Chips?

In the edge computing model, processing power is pushed out to the network’s edge, closer to devices that collect data. Edge computing has grown rapidly as organizations deploy more and more Internet of Things (IoT) devices. Sensors, video cameras and other devices are collecting vast amounts of data that can be used to improve business processes, reduce risk and enable better decision-making. AI applications take this concept to the next level.

Traditionally, the data collected by IoT devices would be sent to a centralized data center or the cloud for processing. Transferring data back and forth over distance takes time, causing latency. Edge computing eliminates this latency, enabling faster processing. This is particularly valuable for AI.

Edge AI requires specialized chips that are powerful enough to handle deep learning algorithms and process large amounts of data. However, they must also be affordable, small enough for IoT devices and capable of conserving battery life. Most edge AI chips are CPUs, but other types are emerging. For example, Google based its Edge TPU on an application-specific integrated circuit (ASIC) architecture. Edge AI chips are now used in smartphones and tablets, wearables, building control systems, and many other devices.

Other Emerging AI Hardware Technologies

In-memory computing is an emerging technology in AI. In traditional computer architectures, data is stored on a disc and called into memory when needed. Memory then gives the CPU access to the data. With in-memory computing, certain computational tasks are performed in memory. This reduces the latency and energy waste of moving data around and significantly improves performance.

A concept called at-memory computing developed by startup Untether uses short, massively parallel direct connections between specially optimized memory and the processor. Graphcore has developed an intelligence processing unit (IPU) that holds the entire machine learning model within the processor. Celestial AI is developing an AI accelerator chip with light-based logic systems.

Hardware Used for Cloud AI Platforms

Because deep learning involves the analysis of large datasets, AI applications can benefit from cloud-based resources. Training an AI model could take weeks or months in a typical data center. Cloud platforms have virtually unlimited hardware capacity that can accelerate the process. This capacity can easily be spun up, spun down, turned on or shut off.

Cloud providers also offer AI software frameworks and toolsets to make development faster and easier. Monitoring services and management tools can help keep AI projects within budget.

The Google Cloud platform comprises hardware based on Google’s Cloud TPUs. It is easy to use and has a free trial program and usage-based pricing. Google Cloud ML Engine is a complete model training and deployment solution. Google also offers data pretraining, pretrained models and various solutions for specific use cases.

AWS designed its Graviton chip to give customers good performance and greater utilization at lower prices. The latest version, Graviton3E, offers significantly better performance than its predecessors, making it suitable for AI. AWS also developed the Inferentia2 accelerator to deliver even higher throughput economically. AWS offers a fully managed machine learning service along with use-case-specific services.

Other cloud AI services use GPUs and other types of AI chips in their hardware, and provide various software frameworks and tools. Options include IBM Cloud, Microsoft Azure and Oracle Cloud. Each offers different capabilities and price points that make them better suited for certain use cases.

Conclusion

AI has the power to transform a wide range of industries, from manufacturing to healthcare to finance. AI applications have become practical due to improvements in computing hardware, and cloud-based offerings make AI capabilities accessible without large investments. Hardware components are advancing rapidly and new technologies are emerging that promise to take AI to the next level.