What is AI hardware? Approaches, benefits and examples


Summary

Artificial intelligence algorithms are increasingly popular in various industries and applications, but their performance is often limited by the processing power of traditional processors.

To improve the capabilities of AI algorithms, some companies are developing new hardware offerings such as graphics processing units (GPUs) and AI accelerators like Google’s TPUs. These new processors can give AI algorithms a significant speed boost and enable better results.

AI-optimized alternatives to the standard processor

The central processing unit (CPU) of a computer is responsible for receiving and executing instructions. It is the heart of the computer and its clock rate determines how fast it can process calculations.

For tasks that require frequent or intense computations, such as AI algorithms, specialized hardware can be used to improve efficiency. This hardware typically does not use different algorithms or inputs, but is designed specifically to handle large amounts of data and provide enhanced computing power.

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Auf der eigenen Grafikkartenkonferenz GTC stellt Nvidia das neueste Mitglied der dedizierten KI-Hardware-Familie für den Rand der Cloud vor.
Nvidia offers AI hardware specifically for robotics with its Jetson family of chips, among other products. | Picture: Nvidia

AI algorithms often rely on processors that can perform calculations in parallel, which allows new AI models to be created more quickly. This is particularly the case with graphics processing units (GPUs), which were originally designed for graphics calculations but have proven effective for many AI tasks due to similarities in computational operations between the image processing and neural networks. To optimize their performance for AI, these processors can also be tailored to efficiently process large amounts of data.

AI hardware for different requirements

Hardware requirements for training and using AI algorithms can vary widely. Training, which involves recognizing patterns in large amounts of data, typically requires more processing power and can benefit from parallelization. Once the model is trained, computational requirements may decrease.

To meet these varied needs, some processor manufacturers are developing AI inference chips specifically for running trained models, although fully trained models can also benefit from parallel architectures.

Traditionally, PCs have separate memory and processors in their layout. However, GPU manufacturers have integrated high-speed memory directly on the card to improve the performance of AI algorithms. This allows compute-intensive AI models to be loaded and run directly on GPU memory, saving time spent on data transfer and speeding up arithmetic calculations.

In addition to traditional CPUs and GPUs, there are also compact AI chips designed for use in devices such as smartphones and automation systems. These chips can perform tasks such as voice recognition more efficiently and with less power consumption. Some researchers are also exploring the use of analog electrical circuits for AI computation, which offer the potential for faster, more accurate, and more energy-efficient computation in a smaller space.

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Examples of AI hardware

Graphics processors, or GPUs remain the most common AI hardware used for processing, especially machine learning tasks. Due to the aforementioned advantage of extensive parallelization, GPUs often provide calculations several hundred times faster than CPUs.

As the demand for machine learning continues to grow, technology companies are developing specialized hardware architectures that prioritize accelerating AI learning algorithms over traditional graphics capabilities.

Nvidia, the market leader in this field, offers products such as the A100 and H100 systems that are used in supercomputers around the world. Other companies, like Google, are also creating their own hardware specifically for AI applications, with Google’s Tensor Processing Units (TPUs) now in their fourth generation.

In addition to these generalized AI chips, there are also specialized chips designed for specific parts of the ML process, such as handling large amounts of data or use in devices with limited space or battery life, such as smartphones.

On top of that, traditional processors are geared to handle ML tasks better by performing calculations at a faster speed, even if that means a decrease in accuracy. Finally, many cloud service providers also allow compute-intensive operations to run in parallel across multiple machines and processors.

Top AI Hardware Manufacturing Companies

Most GPUs for machine learning are produced by Nvidia and AMD. Nvidia, for example, enables more precise computational operations when training ML models using so-called “tensor cores”.

H100 cards also support mixing different precisions with the Transformer Engine. AMD offers its own approach to adapting GPUs to the demands of machine learning with the CDNA-2 and CDNA-3 architectures which will be released in 2023.

Google continues to lead the way in pure ML acceleration with its TPUs, available for rental through the Google Cloud Platform and accompanied by a suite of tools and libraries.

Google TPUv3 server
A Google Cloud server with v3 TPU chips for AI training. | Image: Google

Google relies on TPUs for all of its machine learning-based services, including those in its Pixel line of smartphones. These chips locally handle tasks such as voice recognition, live translation and image processing.

Meanwhile, other big cloud providers like Amazon, Oracle, IBM and Microsoft opted for GPUs or other AI hardware. Amazon has even developed its own Graviton chips, which prioritize speed over precision. Front-end applications such as Google Colab, Microsoft Machine Learning Studio, IBM Watson Studio, and Amazon SageMaker allow users to use specialized hardware without necessarily being aware of it.

Startups are also getting into AI chipsets, with companies like California Matrix D produce in-memory computing (IMC) chips that approximate arithmetic calculations to data stored in memory.

Ein Ausschnitt des riesigen Cerebras Chips, ein bronzefarbenes, strukturiertes Muster mit Punkten, die untereinander verbunden sind
Cerebra’s superchip is designed to dramatically improve AI performance. | Image: Brains

start Detach uses a method called “at memory computing” to achieve high computing power of two petaops in a single smart card. This approach consists of performing calculations directly in the RAM cells.

Other companies, such as Graphcore, Cerebras and Celestialare also exploring in-memory computing, alternative chip designs, and light-based logic systems for faster AI computations.

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