An AI accelerator, more commonly referred to as an AI chip, is a piece of hardware that’s been specifically designed to enhance the speed and efficiency of Artificial Intelligence (AI) and machine learning use cases and workloads.
AI accelerators have become an integral part of AI development as these chips are specially tuned and optimized to crunch the complex computations of AI tasks.
“AI would not exist in its current form if not for the AI accelerators,” says Stefan Leichenauer, VP Engineering, SandboxAQ. “AI is an experimental science, and AI accelerators are to AI as telescopes are to astronomy or microscopes to biology.”
The concept of AI accelerators has evolved over the years. In the early days, AI tasks were usually handed over to conventional CPUs. While they work, conventional CPUs aren’t really adept at handling the intensive computational demands of AI workloads.
However, over the last decade or so, the growth of AI tools made traditional CPUs extremely inefficient in handling and processing AI tasks. This led to calls for specialized hardware tailored for AI-specific tasks, which resulted in the development and proliferation of AI accelerators.
Applications for AI accelerators
AI accelerators are used in a wide range of applications across various industries, primarily to take advantage of their ability to process large amounts of data quickly and efficiently.
For instance, in autonomous vehicles, AI accelerators are used to process vast amounts of sensor data, which enables real-time object detection, tracking, and decision-making. They are also used in natural language processing (NLP) applications such as chatbots, voice assistants, and real-time language translation.
Similarly, AI accelerators also help speed computer vision tasks like image, and facial recognition in applications like surveillance, healthcare, and retail. These capabilities also come in handy in the field of robotics, where these chips help improve decision-making for tasks like object recognition, grasping, and manipulation.
“Many AI accelerators have been optimized to run Large Language Models (LLMs) as those have risen in prominence,” says Leichenauer. “But now we’re seeing that quantitative AI applications and Large Quantitative Models (LQMs) are becoming increasingly important, especially in areas where LLMs are not enough to solve the problem alone.”
He believes applications such as drug discovery, materials design, advanced sensing, and robotics, represent a huge set of high-impact challenges to solve, and AI accelerators that are more geared toward these kinds of applications are starting to emerge as a new direction of development.
Types of AI accelerators
There are a number of different AI accelerators available, each with its own advantages and disadvantages. The most popular AI accelerators include Google TPU v5p, Nvidia A100 and H100, AMD Instinct MI300X, and Intel Gaudi 3. Each of these AI accelerators has its own strengths and weaknesses, so it’s important to choose the right one for your specific needs.
GPUs are perhaps the most widely used type of AI accelerator. Thanks to their parallel processing capabilities, they are a popular choice for training AI models. Compared to specialized accelerators, GPUs are a lot more affordable and still offer adequate performance for many types of AI applications. However, they can be power-hungry and aren’t the best choice for very large-scale applications.
Field programmable gate arrays (FPGAs) are another popular type of AI accelerator. Although more expensive than GPUs, FPGAs are often used for real-time AI applications such as autonomous vehicles. The best thing about FPGAs is that they are highly customizable AI accelerators that can be reconfigured to perform different tasks, which makes them very versatile.
Application-specific integrated circuits (ASICs) are purpose-built chips designed with a specific purpose or workload in mind. Unlike FPGAs, ASICs cannot be reprogrammed, but since they are constructed with a singular purpose, they typically outperform other, more general-purpose accelerators. ASICs offer the best performance of any type of AI accelerator, but they’re also the most expensive. ASICs are typically used for large-scale AI applications such as deep learning.
Neural Processing Units (NPUs) are AI accelerators optimized for use in neural networks, and deep learning. NPUs can process large amounts of data faster than other chips, which makes them ideal for tasks like image recognition, and natural language processing.
Benefits of using an AI accelerator
AI accelerators help advance the AI realm, by ironing out the computational bottlenecks in AI workloads. Unlike CPUs and GPUs, these AI chips are purpose-built for AI applications. There are many benefits of using an AI accelerator, but here are some of the most important ones:
Speed: One of their primary features is their dramatically lower latency over conventional processing chips, which helps them process and analyze large amounts of data. This makes them essential for AI applications such as autonomous vehicles, healthcare, and finance.
Efficient: In addition to their lower latencies, AI accelerators are architectured in a fashion that makes them anywhere from one hundred to a thousand times more efficient than traditional chips. They also have parallel computing capabilities that help reduce the time and resources required for AI tasks such as training and inference.
Cost-effective: While they may be more expensive than traditional chips, in the long run, AI accelerators turn out to be more cost-effective than traditional computing chips, especially when it comes to large-scale AI workloads.
What makes AI accelerators special?
As we’ve said, AI accelerators are specialized hardware devices that help facilitate the execution of AI workloads efficiently. They deliver superior performance as compared to traditional computing chips, and they do this while minimizing energy consumption. Here’s what makes them unique:
Parallel Processing: AI accelerators leverage parallel processing architecture to execute multiple computational tasks simultaneously. They do this through the use of thousands of small processing cores, often referred to as processing elements (PEs) or compute units. This helps maximize throughput and reduces processing times by an order of magnitude.
Reduced-Precision arithmetic: To save power, AI accelerators typically operate on low-precision floating-point formats, using 16-bit or even 8-bit floating-point numbers, instead of the 32-bits that are used by general-purpose chips. This also helps reduce memory bandwidth and energy consumption while maintaining the standards required for all kinds of AI workloads.
Dataflow Architecture: Data in an AI accelerator is processed in a pipeline fashion, with each stage performing a specific operation. This not only allows these chips to handle large datasets efficiently but also cuts down memory access latency.
Memory Hierarchy: Similarly, AI accelerators also have a tuned memory hierarchy, which includes on-chip memory, such as registers, and caches, as well as off-chip memory, like DRAM. This hierarchy helps facilitate rapid data access and also minimizes latency during complex AI computations.
Software Optimization: To fully leverage their capabilities, AI accelerators require software optimization. This includes everything from library optimizations for specific AI frameworks, to runtime optimizations.
Integration with CPUs and GPUs: AI accelerators can also be integrated with CPUs and GPUs to create heterogeneous computing systems. This allows them to efficiently juggle workloads between the available hardware in order to optimize performance and reduce power consumption.
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