An artificial intelligence (AI) accelerator is computer hardware that specifically handles AI requirements. It speeds up processes, such as artificial neural network (ANN) tasks, machine learning (ML), and machine vision.
Back in the 1980s, graphics accelerators made PCs faster and more efficient by freeing up the main processor and handling all the graphics requirements. Similarly, AI accelerators free up the main processor from having to deal with complex AI chores that can be resource-intensive.
Other interesting terms…
Read More about an AI Accelerator
AI accelerators are critical in speeding up data processing to create AI applications at scale. Learn more about them below.
How Have AI Accelerators Evolved?
Attempts to develop a standard AI hardware accelerator started as early as the 1990s with neural network accelerators. Let’s take a look at how this aspect of AI has evolved over the years.
Digital Signal Processors
From 1988 to 1993, the Adaptive System Research Department at Bell Labs developed a convolutional neural network that was able to recognize handwritten digits accurately. The use of Digital Signal Processors (DSPs) was one of the first attempts in developing an AI accelerator, and you can view the actual footage of the 1993 demo in the video below.
Graphics Processing Units
Graphics processing units (GPUs) have been widely used for AI and ML work, even though these were intended to process images and their properties primarily. But since neural networks and image manipulation have a quite identical mathematical basis, GPUs have increasingly been used for AI acceleration from 2000 to 2010.
Field-Programmable Gate Array-Based Accelerators
Field-programmable gate array (FPGA) technology was tested for AI functions in the 1990s and has begun being adopted to accelerate ML and deep learning in 2016. FPGA-based accelerators are more power-efficient compared with other AI accelerators. They are also more flexible because they have programmable components.
Application-Specific Integrated Circuits
An application-specific integrated circuit (ASIC) is an integrated circuit (IC) chip that was made for a specific use, unlike FPGA-based accelerators and GPUs. ASICs were tailor-made for AI functions. As such, they can be better than FPGA-based accelerators and GPUs in terms of performance. However, an ASIC is very expensive to develop. But that has not stopped companies from using it for AI acceleration since 2024.
Heterogeneous Computing Architecture
Although not initially developed for AI acceleration, since at least 2024, heterogeneous computing devices, such as cell microprocessors, have been used to carry out several AI tasks. For instance, the cell microprocessor architecture was notably used to predict successful weight loss in people who were obese. A cell microprocessor is a multicore microprocessor that uses a regular PowerPC core together (i.e., a type of main core) with other highly specialized coprocessors.
What Are the Types of AI Accelerators?
AI accelerators come in several types—water-scale integrated chips, neural processing units (NPUs), GPUs, FGPAs, and ASICs. We already described GPUs, FGPAs, and ASICs earlier so let us focus on the remaining kinds here.
Neural Processing Units
NPUs are AI accelerators meant for deep learning and neural networks. They are built to meet these processes’ data processing requirements. They can process huge datasets fast and perform many AI tasks associated with ML like image recognition. They are also the neural networks behind popular AI and ML applications like ChatGPT.
Water-Scale Integrated Chips
Wafer-scale integrated chips are “super” chips built using the water-scale integration (WSI) process. WSI is a means to transform very large AI chip networks into a single super chip that helps users reduce costs and accelerate deep learning model performance. The most popular example of this chip is Cerebras’s WSE-3 chip network, which was built using Taiwan Superconductor Manufacturing Company (TSMC)’s 5 nm process. At present, WSE-3 is the world’s fastest AI accelerator.
What Are the Benefits of Using AI Accelerators?
Here are some pluses associated with using AI accelerators.
Use Less Energy
AI accelerators can be 100–1,000 times more energy-efficient than general-purpose computers. They are ideal for use in data centers that should remain cool because they do not consume too much power or dissipate too much heat even when performing large amounts of calculation.
Speed Up Computing
Given their speed, they lower latency, which is especially important in safety-critical applications like the advanced driver assistance systems (ADASs) found in self-driving cars.
Scale Up or Down Fast
Writing an algorithm to process one problem is hard. So, imagine what it would be like to use several algorithms. AI accelerators can speed up this process, allowing you to add more systems to your infrastructure should the need arise.
Use Different Architectures Simultaneously
When you split AI tasks into subtasks for faster processing, each may require a different architecture. Thankfully, AI accelerators can work together despite having several architectures.
What Are Some Challenges in AI Accelerator Development?
Despite the benefits of using AI accelerators, their usage is still hampered by some challenges that we named below.
- Around 90% of AI accelerators are manufactured in Taiwan.
- Many of the most powerful AI models require more computational power than most AI accelerators can handle.
- AI accelerators are small, making it hard to match the amount of energy they need to power up.
Key Takeaways
- An AI accelerator is computer hardware that specifically handles AI requirements.
- It speeds up processes, such as ANN tasks, ML, and machine vision.
- It has gone through several evolutionary stages and taken on various forms—from digital signal processors to GPUs to what we know as AI accelerators today.
- It is energy-efficient, scalable, and can use several architectures at once.
- Apart from GPUs, FGPAs, and ASICs, super chips and NPUs are AI accelerators, too.
Sources
- https://www.synopsys.com/glossary/what-is-an-ai-accelerator.html
- https://www.ibm.com/think/topics/ai-accelerator
- https://en.wikipedia.org/wiki/AI_accelerator
- https://www.intel.com/content/www/us/en/learn/ai-accelerators.html
- https://www.linkedin.com/pulse/ai-accelerators-driving-efficiency-performance-machine-ravi-naarla/
