AI accelerators

Artificial intelligence (AI) is transforming how we interact with technology, from smartphones to autonomous vehicles to massive data centers powering enterprise AI.

To power these intelligent systems, AI accelerators play a critical role in speeding up model training and inference, enabling faster, more efficient AI performance across devices and industries.

Learn more about what AI accelerators are, how they work and where they are used, or connect with our Sales Support team to find out more.

What are AI accelerators?

AI accelerator definition: An AI accelerator is a specialized hardware component that improves the speed and efficiency of AI workloads by optimizing compute performance and power consumption.

AI accelerators emerged as AI workloads scaled beyond what general-purpose processors could efficiently support, shifting performance bottlenecks from computation to data movement, memory bandwidth and latency. AI technologies are becoming increasingly commonplace, with AI now accessible across a wide range of devices — from smartphones and laptops to industrial systems and data centers.

As adoption grows, so do the demands on AI applications, requiring faster, more efficient processing to deliver real-time insights and intelligent functionality.

What's changed is that modern AI performance no longer scales with compute alone. It increasingly scales with how efficiently data can be moved, accessed and supplied to processors. As models grow in size and complexity, accelerators may stall while waiting on data, making bandwidth, latency and locality key factors in overall system performance.

In consumer devices like smartphones, AI accelerators enable advanced features such as voice recognition, image processing and predictive analytics — all while maintaining low latency and high energy efficiency.

One of the key advantages of using AI accelerators is their ability to optimize power usage. By streamlining data processing and reducing computational overhead, they help minimize energy consumption — an essential benefit for mobile and edge environments, where power constraints are critical.

AI acceleration also supports scalability. As models grow in complexity and data volumes increase, accelerators allow systems to handle larger workloads without compromising performance. This scalability is vital for deploying AI across diverse environments — from edge devices to enterprise data centers — where speed, efficiency and adaptability are essential.

How do AI accelerators work?

AI accelerators enhance computational performance by executing large numbers of operations in parallel and optimizing how data moves between compute units and memory — two critical factors in speeding up AI model training and inference.

In practice, accelerator performance depends not only on parallel compute capability, but on how efficiently data can be supplied from memory — because stalled data paths leave compute resources underutilized.

AI accelerators are purpose-built semiconductor devices that use highly optimized circuits to perform massive numbers of operations in parallel, enabling real‑time processing of data‑intensive workloads with high performance and energy efficiency.

Depending on the manufacturer and application, AI accelerators can take various forms, including graphics processing units (GPUs), neural processing units (NPUs), field-programmable gate arrays (FPGAs) and application-specific integrated circuits (ASICs). Despite their architectural differences, they share a common goal: to deliver elevated processing power while minimizing the overhead of moving data through the system.

By boosting computational throughput and reducing latency, AI accelerators allow systems to generate outputs more quickly while consuming less energy. This balance of speed and efficiency is essential for deploying AI across both edge devices and large-scale data centers, where power, thermal limits and data movement increasingly define overall system performance.

What is the history of AI accelerators?

The evolution of AI accelerators parallels the broader development of AI and computing technologies. While AI has gained widespread prominence in the 21st century, its foundations — and the hardware designed to support it — date back several decades.

• 1970s, early coprocessors: The first computer accelerators emerged as coprocessors, designed to offload specific tasks from the central processing unit (CPU). These early innovations laid the groundwork for more specialized acceleration technologies.
• 1990s, neural accelerators: As AI research advanced, particularly in neural networks, hardware accelerators were introduced to improve the efficiency of training and inference. These early neural accelerators helped push the boundaries of what AI systems could achieve.
• 2010s, specialized AI accelerators: The rise of deep learning and real-time AI applications drove the development of targeted accelerators. Technologies like FPGAs, NPUs and GPUs became essential for handling specific AI workloads, from visual data to complex neural computations.
• 2020s, growth of AI acceleration: AI acceleration has increasingly diverged based on deployment environment and performance requirements. In data center and AI training environments, accelerators evolved to sustain massive parallel workloads, where performance is constrained by the ability to move data at scale — driving the adoption of high‑bandwidth memory (HBM). In consumer, mobile and edge devices, AI accelerators prioritized low‑latency inference and power efficiency, relying on memory architectures optimized for energy and cost rather than peak bandwidth.

What are the key types of AI accelerators?

Central processing units

CPUs are the foundational processors in all computing systems, responsible for managing a wide range of general-purpose tasks. While some CPUs are enhanced to support AI workloads, they are not specifically optimized for the parallel processing demands of modern AI models. As a result, they may struggle to meet the performance and efficiency requirements of advanced AI applications.

In many AI systems, CPUs continue to play an important role by orchestrating workloads, coordinating data movement and managing control-plane tasks that support accelerator operation.

Graphics processing units

GPUs are designed for high-throughput parallel processing, making them ideal for handling visual data and deep learning tasks. Widely used in AI training and inference, GPUs excel at executing multiple operations simultaneously, which is especially valuable in computer vision and large-scale model development.

Because training and high-throughput inference are often bandwidth-intensive, GPU performance in data center and AI training environments is closely tied to the memory subsystem's ability to supply data efficiently and consistently.

Neural processing units

NPUs are purpose-built for AI workloads, particularly neural network operations. They enable real-time data processing with minimal power consumption, making them well-suited for edge devices and mobile platforms. NPUs deliver both computational and energy efficiency, which are critical for deploying AI at scale.

Edge AI accelerators

Edge AI accelerators are embedded in devices such as smartphones, industrial sensors and Internet of Things (IoT) systems, where AI workloads prioritize low-latency inference and power efficiency over peak compute throughput. These accelerators enable local AI processing — reducing latency, improving responsiveness and minimizing reliance on cloud infrastructure. By bringing intelligence closer to the data source, edge AI accelerators support real-time decision-making in resource-constrained environments where energy efficiency and responsiveness are critical system-level requirements.

How are AI accelerators used?

AI accelerators are used across a wide range of applications, each with distinct performance, latency and power requirements:

• Edge devices (smartphones and IoT): AI accelerators enable complex models to run locally on devices with limited computational resources, supporting real-time decision-making without relying on cloud infrastructure.
• Large language models (LLMs): Training and deploying LLMs requires massive parallel compute and high memory throughput, making accelerators such as GPUs and NPUs essential.
• Real-time AI applications: Chatbots and virtual assistants depend on accelerator performance to deliver low-latency, responsive user experiences.
• Autonomous driving and advanced driver-assistance systems (ADAS): AI accelerators process continuous streams of sensor and visual data, enabling split-second decisions in dynamic, safety-critical environments.

Across these use cases, the architectural pattern is consistent: AI accelerators improve performance not only by increasing compute throughput, but by enabling systems to move, stage and access data more efficiently — because feeding compute reliably is often the limiting factor in real-world AI deployments.

As AI architectures continue to favor highly parallel processing, demand for memory bandwidth and data locality is expected to increase, making memory performance a persistent constraint rather than a short-term scaling challenge.