What Is an AI Accelerator?

Many of today’s AI computing tasks require additional, specialized AI processing capabilities to deliver desirable results.

To meet these emerging demands, technologists take advantage of AI accelerators, which can be either discrete pieces of hardware incorporated into their solution design or built-in features in the CPU. Both forms of AI accelerators provide supercharged performance for AI workloads. They’re employed across today’s IT and AI landscape, with use cases in client computing devices, edge environments, and data centers of all types.

Discrete hardware AI accelerators are most commonly used alongside CPUs in the parallel computing model, though select technologies can also be used in stand-alone architectures. Some single-package CPU/accelerator offerings are also available on the market.

Integrated AI accelerators play an important role in enabling AI on modern CPUs. These built-in capabilities provide optimized performance for specific functions or operations, such as vector operations, matrix math, or deep learning. In some cases, integrated AI accelerators can enable AI without requiring specialized hardware.

Architects and developers include both types of AI accelerators in their solution designs when they need to support demanding use cases with extensive throughput and latency needs.

Role of Discrete Hardware Accelerators in AI

Most commonly, discrete hardware AI accelerators augment the capabilities of the CPU to handle the challenges of demanding AI workloads. This approach, called parallel computing, allows the two compute units to work together to tackle complex problems. By taking advantage of parallel computing, CPUs and discrete hardware AI accelerators used in tandem can significantly reduce processing times for AI workloads. In some cases, discrete hardware AI accelerators can be used independently without working alongside a CPU.

Types of Hardware AI Accelerators

From a hardware perspective, the term AI accelerator can refer to general-purpose components such as graphics processing units (GPUs) used for AI, field-programmable gate arrays (FPGAs) used for AI, and to AI-specific offerings such as neural processing units (NPUs) and tensor processing units (TPUs).

When discussing AI accelerators and AI processors, it’s important to note that this is a developing area with many vendor-specific terms. For many of these technologies, common descriptors and standardized language have not yet emerged.

GPUs

Many associate GPUs with gaming and advanced 3D rendering tasks, but they can also provide powerful acceleration capabilities for AI workloads. They are among the most widely used and affordable hardware AI acceleration technologies. GPUs are being used to power AI applications, including machine learning, deep learning and computer vision.

FPGAs

FPGAs are unique compared to CPUs in that they can be reprogrammed at the hardware level to fit specific needs, offering significant versatility as requirements change over time.

FPGAs are used in parallel computing architectures to fuel AI performance. They’re especially suited for edge AI, where diverse I/O protocols, low-latency capabilities, low power consumption, and long deployment lifetimes make them ideal for industrial, medical, test and measurement, aerospace, defense, and automotive applications. FPGAs can also be used to support networking and data center use cases.

NPUs

Built for neural network operations, NPUs are specialized hardware AI accelerators used to accelerate deep learning algorithms. Compared to CPUs and GPUs, NPUs are extremely efficient options for AI. They also offer fast speeds and high bandwidth, which make them ideal for integration into fast-moving workflows, such as rapidly generating images or responding to voice commands.

Purpose-Built AI Accelerators

Outside of FPGAs, GPUs, and NPUs, there are also a variety of unique silicon products on the market that deliver powerful, specialized AI performance for a range of use cases. These purpose-built processing solutions can often be deployed in a stand-alone architecture or used to augment CPU capabilities like the other accelerators mentioned in this article.

Benefits of Discrete Hardware AI Accelerators

Discrete hardware AI accelerators offer benefits across the AI workflow that can help speed time to value for AI initiatives.

Energy Efficiency

Sustainability and power usage are key concerns for AI initiatives. Because hardware AI accelerators pack extreme AI performance into a single device, organizations can use them to deliver the computational horsepower required by AI with fewer nodes. This reduced footprint can lead to lower energy consumption.

Accelerated Performance

Getting rapid insights, responses, or training results from AI requires optimized compute that minimizes latency and speeds processing times. Hardware AI accelerators deliver the specialized compute capabilities required by AI workloads to unlock faster AI outputs and better business results.

Scalability

Many accelerators, particularly purpose-built AI hardware, offer additional capabilities that make them ideal for the large-scale environments required by high-complexity AI workloads. These scalability features can include memory capacity and elevated numbers of high-capacity Ethernet ports, helping to fuel the connectivity needs of massive AI and HPC systems.

Role of Integrated Accelerators in AI

Integrated AI accelerator engines are built-in CPU features that provide optimized AI performance, often for specific AI workloads or types of operations. NPUs can also be integrated into CPU architectures to help accelerate AI performance.

Because integrated AI accelerators help alleviate the need for including specialized hardware in a solution design, they’re a great option for those looking to deploy lean, cost-effective AI that can still meet performance requirements. Integrated AI accelerators can be used to enhance a number of AI workloads from edge to cloud—including natural language processing, recommendation systems, image recognition, generative AI, and machine learning.

Benefits of Integrated AI Accelerators

From training to inference, integrated AI accelerator technologies help organizations achieve outstanding AI results with stand-alone CPU architectures.

Optimized AI Performance

Built-in AI acceleration enables CPUs to meet the advanced performance requirements of many critical AI use cases.

Reduced Hardware Costs

Integrated accelerators empower organizations to enable AI with a minimal hardware footprint. Built-in capabilities allow organizations to run many training and inferencing workloads without investing in discrete accelerators, ultimately leading to more-efficient AI solution designs.

Improved Energy Efficiency

Built-in accelerators significantly improve performance per watt to help reduce power consumption and minimize the environmental impact of AI.

Simplified Development

Taking advantage of integrated AI acceleration allows solution architects to avoid the added complexity introduced by specialized hardware. It also helps minimize the need for code or application changes.

AI Accelerators Solutions

The increasing adoption of AI means that AI accelerators are being deployed at virtually every layer of the technology landscape:

• For end user devices, GPUs and integrated NPUs are commonly used to boost AI workload performance.
• At the edge, FPGAs offer flexibility and efficiency benefits that can help extend AI capabilities to more places.
• In the data center, both GPUs and purpose-built AI accelerators are being used at scale to power extremely complex AI workloads like financial modeling and scientific research.
• Integrated AI accelerators are available in select CPU offerings, with options available across edge, data center, cloud, and client computing.

As AI becomes more prevalent and advanced, both types of AI accelerators will continue to play an important role in supporting next-gen capabilities.