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

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:

AI Accelerator Types

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

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 Units – Google’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.

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.

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