AI Accelerator

What is an AI Accelerator?

An AI accelerator is a type of specialized hardware or software that is designed for running AI algorithms and applications with high efficiency.


Organizations can use AI accelerators as a tool to optimize the performance of AI solutions during training and inference tasks. This reduces the amount of time and computational resources needed to train and run solutions like large language models (LLMs).

One recent example of an AI accelerator is Nvidia’s series of Blackwell-architecture GPUs, which come with 208 billion transistors and can support training and inference for AI models scaling up to 10 trillion parameters.

Techopedia Explains the AI Accelerator Meaning

Techopedia Explains the AI Accelerator Meaning

An AI accelerator, which means a type of hardware or software optimized specifically for running AI workloads, uses a technique called parallel processing

Parallel processing involves using multiple processors to handle different parts of a task at once. Such approaches increase the speed with which inference takes place. 

In contrast, traditional computing systems like CPUs process tasks one at a time, considerably decreasing the amount of time that it takes to train a neural network.

History of AI Accelerators

The development of processors and hardware designed to support AI use cases can be traced as far back as the 1980s, most notably when Intel launched an analog processor known as the ETANN 80170NX. This neural processor was designed specifically for deploying neural networks.

Similarly, as far back as 1987, Dallas Morning News had reported that Bell Labs had developed “A chip that implements neural computing, a new approach to developing computers capable of performing tasks like the human brain.”

Later in the 1990s, researchers were experimenting with FPGA-based accelerators in inference and training activities, to try and make neural networks more scalable.

By 2009, Stanford University researchers Rajat Raina, Anand Madhavn, and Andrew NG released a paper highlighting how modern GPUs surpassed multicore CPUs in deep learning tasks – underlying the value of an alternative computing approach.

How AI Accelerator Works

There are many different types of AI accelerators, which all work differently and are optimized for unique requirements and tasks. That being said, accelerators have a number of core features.

These include:

Parallel Processing Architecture
Accelerators use a parallel processing architecture to complete multiple computational tasks at the same time.

Large-Scale Data Processing
Accelerators are optimized to be able to process large datasets and run more complex algorithms.

On-Chip Memory Hierarchy
The use of an on-chip memory hierarchy enables quicker access to frequently used data.
Reduced Precision Arithmetic
Many accelerators use a technique called reduced precision arithmetic to conduct calculations in 16-bit or 8-bit, to speed up computation without significantly reducing accuracy.
Energy Efficiency
These components aim to achieve maximum performance while using minimal power.

AI Accelerator Types

As mentioned above there are many different types of AI accelerators that organizations can implement. These include:

Graphics Processing Units (GPUs)Tensor Processing Units (TPUs)Field-Programmable Gate Arrays (FPGAs)Application-Specific Integrated Circuits (ASICs)Neuromorphic ChipsEdge AI Accelerators

GPUs are often used for AI processing due to their ability to support parallel processing, which makes them ideal for deep learning.

Google’s TPUs are designed to accelerate workloads based on TensorFlow, giving users the option to use them for neural network inference and training tasks.

FPGAs are hardware circuits or chips that can be reprogrammed to perform AI computation tasks.

ASICs are integrated circuits or custom chips that can be used for AI computation with high performance and efficiency.

Neuromorphic chips are chips that emulate the structure of a biological neural network, such as the human brain, and are designed to mimic human cognition.

Edge AI accelerators are chips that are designed to enhance inference performance on edge devices such as smartphones and Internet of Things (IoT) devices.

AI Accelerator Examples

There are a number of different real-world examples of AI accelerators. These include:

  • Nvidia H200 Tensor Core GPU – The Nvidia H200 series of GPUs is an example of a GPU that’s been optimized for generative AI workloads.
  • Google Cloud Tensor Processing UnitsGoogle’s Cloud offers custom-built tensor processing units, which are designed for training and inference tasks for large AI models.
  • Intel Agilex’s 9 FPGAs – Intel’s Agilex 9 FPGAs are an example of FPGA solutions with programmable logic.
  • AMD Alveo MA35D – AMD’s Alveo MA35D is an ASIC-based media accelerator with an integrated AI processor that’s designed to power steaming services.
  • Intel Loihi 2 Research Chip – Intel’s Loihi 2 chip is a neuromorphic chip that has been tested in use cases such as adaptive robot arm control and visual-tactical sensory perception.

AI Accelerator Pros and Cons

Using AI accelerators comes with a number of core pros and cons.


  • Better performance
  • Less latency
  • Run more advanced applications
  • Scalable
  • Energy efficiency
  • Increased cost-effectiveness


  • Difficult to deploy
  • High cost
  • Inconsistency
  • Power consumption

The Bottom Line

AI accelerators have an important role to play in making sure that organizations have the computational throughput to get the most out of AI.

Ultimately, the fewer resources it takes to conduct inference and training tasks, the more accessible deep learning becomes to the average organization.


What is an AI Accelerator in simple terms?

What do AI accelerators do?

What is the difference between a GPU and an AI accelerator?

What is the difference between an AI processor and an AI accelerator?

What is an example of an AI accelerator?


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Tim Keary
Technology Specialist

Tim Keary is a freelance technology writer and reporter covering AI, cybersecurity, and enterprise technology. Before joining Techopedia full-time in 2023, his work appeared on VentureBeat, Forbes Advisor, and other notable technology platforms, where he covered the latest trends and innovations in technology. He holds a Master’s degree in History from the University of Kent, where he learned of the value of breaking complex topics down into simple concepts. Outside of writing and conducting interviews, Tim produces music and trains in Mixed Martial Arts (MMA).