What is AI hardware?

AI systems, such as large language models (LLMs) or neural networks, require high-bandwidth hardware to process the types of large datasets used by machine learning (ML), deep learning (DL) and other types of AI algorithms to replicate the way human beings think, learn and solve problems. 

While general-purpose hardware, such as the common central processing unit (CPU), can carry out most computational tasks, AI infrastructure requires significantly more computational power. For heavy AI workloads, such as the kind associated with AI development and AI training, AI hardware like AI accelerators and AI chips offers certain optimizations better suited for scalability and ecosystem optimization.

As the industry continues to advance rapidly, AI breakthroughs are making headlines almost every day. As we appear to be entering an AI age, AI hardware makes up the critical infrastructure components powering these impressive AI applications.

Facial recognition, for example, an AI application we almost take for granted, relies heavily on AI hardware to be able to work. It must locally process your image, check it against approved images and recognize and identify any normal variations to unlock your phone or tablet. Without AI hardware, technology such as face ID or smart assistants would be less practical and more expensive.

The following are some of the main benefits of AI hardware:

The most common type of AI hardware is AI chips, advanced semiconductor microchip processors that function like specialized CPUs. Major manufacturers like Nvidia, Intel and AMD, as well as startups such as Cerebras Systems, design these integrated circuits featuring different types of chip architectures. Doing so better suits various types of AI solutions, increases energy efficiency and decreases bottlenecks.  

Although AI hardware also includes other types of next-generation hardware, such as low-latency interconnects to deliver real-time compute results, the two main categories of AI hardware are processors and memory.

High-performance processors such as graphics processing units (GPUs), tensor processing units (TPUs), neural processing units (NPUs), application-specific integrated circuits (ASICs) and field programmable gate arrays (FPGAs) are the most common types of AI hardware. Processors, like AI chips, provide the computational power to handle complex AI workloads. While these powerful processors often come at the cost of high power consumption, continued advancements in AI hardware are striving to improve energy efficiency with every new cycle.   

• GPUs: Originally designed for rendering graphics, graphics processing units (GPUs) use parallel processing that is also well suited for AI tasks like inference and training.

• ASICs: ASICs are highly specialized processors designed and manufactured for specific applications. These types of AI chips are custom-built not only for AI, but for whatever unique application a particular AI solution might be used for. Because these types of chips are made for specific applications, they are not as flexible as some of their alternatives. However, for the same reason, they can offer the most optimized performance. 

• FPGAs: Field programmable gate arrays (FPGAs) can be thought of as reprogrammable ASICs. While an ASIC is programmed once and cannot be changed, FPGAs must be programmed before use and can be reprogrammed and tweaked again and again. These types of chips are highly versatile and are often used to prototype new ASICs. 

• NPUs: Neural processing units (NPUs) are a type of ASIC built to specifically improve deep learning and neural networks. NPUs are used to perform data-intensive tasks such as computer vision and natural language processing (NLP).

• TPUs: TPUs are a proprietary type of AI accelerator designed by Google to handle tensor math operations common to AI applications. Google’s TPUs are purpose-built to train large language models and generative AI.

• WSE-3: By some benchmarks, Cerebras Systems’ wafer scale engine 3 (WSE-3) is the fastest processor ever made, with 900,000 AI cores on one unit. Every core has access to 21 petabytes per second of memory bandwidth for high-speed, large-scale data processing. 

• Telum II: An improvement on IBM’s first AI chip, IBM Telum® II processors are tailor-made for improving AI operations, especially when paired with IBM Spyre™ AI Accelerator. 

Memory plays a critical role in the infrastructure of most computers, including AI-enabled machines. Memory storage architectures and devices ensure that AI systems have fast access to the data and instructions needed to complete AI tasks. A system’s memory capacity and speed directly impact performance capability. Insufficient memory can create bottlenecks that slow or impede all system operations, not just AI workloads.

However, not all memory is created equal and while all memory types have their place, some are better optimized for specific and general AI applications than others. Within AI systems, different types of memory are often used together for different parts of the AI process, with specific requirements depending on the unique project or operational demands. 

• RAM: The main type of memory for both AI and general-purpose computing, random access memory (RAM) offers fast, temporary data storage for active operations. RAM can read and write data quickly, making it well suited for real-time processing. Limited capacity and high volatility make it less ideal for larger-scale AI operations. 

• VRAM: Video RAM (VRAM) is a type of specialized RAM used specifically in GPUs. VRAM offers the kind of improved parallelism desirable for complicated AI tasks; however, compared to regular RAM, it can be more expensive with even less capacity. 

• HBM: Originally developed for use in high-performance computing, high-bandwidth memory (HBM) is designed for lightning-fast data transfer between processing units, a valuable advantage for AI applications. Although HBM’s speed can come with a high price tag, this type of high-speed memory is an ideal choice for AI applications. 

• Nonvolatile memory: Volatile types of memory require constant power to retain data, while nonvolatile memory, like solid-state drives (SSDs) and hard disk drives (HDDs) offer long-term storage without maintenance or power. Clocking speeds far slower than RAM or VRAM, nonvolatile memory isn’t a good option for active data transfers, but it is useful in AI systems for retaining data long term. 

The use cases for AI hardware are as wide and expansive as AI itself. Just as AI technology has borrowed hardware from high-end graphics processing and high-performance computing, these technologies are now using AI hardware to improve their own operations. From data centers to fast food drive-thrus, AI hardware is useful for any and every application of AI technology.

In fact, you might be using AI hardware to read this article. AI chips are increasingly popping up in laptops and mobile devices from manufacturers like Apple and Google, used to increase performance for mobile AI tasks like voice recognition and photo editing. AI hardware is growing powerful and compact enough to handle many of these tasks locally, decreasing bandwidth and improving the user experience.  

Elsewhere, AI hardware is becoming a valuable component in cloud computing infrastructure. Enterprise-level, AI-enabled GPUs and TPUs can be prohibitively expensive, but providers like IBM, Amazon, Oracle and Microsoft offer rentable access to these powerful processors through their cloud services as a cost-effective alternative. 

Some additional applications for AI hardware include the following.

AI hardware is a critical component in the development of self-driving cars and autonomous vehicles. These vehicles use AI chips to process and interpret large volumes of data from cameras and sensors, enabling real-time reactions that help prevent accidents and ensure the safety of passengers and pedestrians.

AI hardware offers the kind of parallelism necessary for things like computer vision, which helps computers “see” and interpret the color of a stop light or the traffic in an intersection.

Edge computing is a rapidly growing computing framework that moves enterprise applications and surplus compute power closer to data sources like Internet of Things (IoT) devices and local edge servers. As our digital infrastructure becomes increasingly dependent on cloud computing, edge computing offers improved bandwidth speeds and stronger security for those concerned with increased privacy.

Similarly, edge AI seeks to move AI operations closer to users. AI hardware is becoming a useful component in edge infrastructure, utilizing machine and deep learning algorithms to better process data at the source, reducing latency and decreasing energy consumption.

Although AI technology has been in development for decades, it was only recently that it really leapt into the spotlight, due in part to breakthrough gen AI technologies like ChatGPT and Midjourney. Tools like these use large language models and natural language processing to interpret natural speech and produce new content based on user inputs.