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autonomous vehicles, medical imaging, speech recognition, etc.):

Artificial intelligence (AI) has been gaining popularity in recent years, and it’s no secret why. It has the potential to revolutionize the way we live our lives, from smart home devices to self-driving cars. However, the processing power required for these tasks is staggering, and traditional computer hardware isn’t always up to the task. That’s where AI hardware accelerators come in. In this article, we’ll explore what AI hardware accelerators are, how they work, and some specific examples of how they’re being utilized in different industries.

What are AI hardware accelerators?

AI hardware accelerators, as the name implies, are hardware devices specifically designed to accelerate the processing of AI workloads. AI workloads are different from traditional workloads in that they require much more simultaneous processing of data than traditional computing tasks. This is where the traditional CPU can struggle.

AI hardware accelerators come in a few different varieties, but they all share the same goal of making AI workloads faster and more efficient. Some of the most popular types of AI hardware accelerators include Graphics Processing Units (GPUs), Field Programmable Gate Arrays (FPGAs), and Application-Specific Integrated Circuits (ASICs).

How do AI hardware accelerators work?

AI hardware accelerators work by reducing the processor’s workload when it comes to AI-specific tasks. CPUs are generalized pieces of hardware, built to handle a wide range of different processing tasks, and they do so quite well. However, when it comes to AI workloads, they can struggle to keep up due to the sheer volume of calculations required.

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In contrast, AI hardware accelerators are purpose-built for these workloads. They’re designed to efficiently perform the repetitive calculations required for AI-specific tasks, freeing up the CPU to handle other processing tasks.

To put it in a real-life scenario, let’s say you have a smart speaker that you use to play music, set reminders, and answer questions. All of these tasks can be handled by a traditional CPU quite easily. However, if you were to ask your smart speaker to analyze your mood based on your choice of music, that’s where an AI hardware accelerator would come in.

The accelerator would take care of the necessary calculations for this task, leaving the CPU free to handle your other requests. As a result, your smart speaker would be more responsive and better able to handle a wide range of requests.

Examples of AI hardware accelerators in specific domains

So now that we know what AI hardware accelerators are and how they work, let’s take a look at some specific examples of how they’re being used in different industries.

Healthcare

In the healthcare industry, AI hardware accelerators are being used to improve the speed and accuracy of medical diagnoses. For example, OpenAI, a research company, has developed an AI system that can diagnose pneumonia from chest X-rays with the same accuracy as a radiologist, but much faster.

The AI system uses an NVIDIA GPU to perform the necessary calculations quickly and efficiently, allowing for quick diagnoses that can save lives.

Finance

The finance industry is also no stranger to AI hardware accelerators. In fact, many financial institutions use FPGAs to handle high-frequency trading. High-frequency trading involves making rapid trading decisions based on incoming market data. FPGAs are ideal for this task because they can perform the necessary calculations quickly and accurately, allowing traders to make informed decisions in real-time.

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Automotive

Self-driving cars are one of the most exciting applications of AI, and they wouldn’t be possible without AI hardware accelerators. Self-driving cars must process vast amounts of data in real-time, including data from cameras, lidar, and radar sensors. NVIDIA GPUs are commonly used in self-driving car applications due to their ability to process this data quickly and efficiently.

Retail

The retail industry is also utilizing AI hardware accelerators to improve the customer experience. For example, Amazon’s Alexa is powered by an NVIDIA GPU, allowing it to process voice commands quickly and accurately. This makes it a valuable tool for shopping online, as customers can easily order products with their voice.

The bottom line

In conclusion, AI hardware accelerators offer a powerful tool for processing the vast amounts of data required for AI workloads. They’re being used in a wide range of industries, from healthcare to finance to retail, and they’re helping to improve the speed and accuracy of a wide range of tasks.

As AI continues to advance, it’s likely that we’ll see even more applications of AI hardware accelerators in the future. From virtual assistants to self-driving cars, these tools will continue to play a critical role in making AI more accessible and efficient than ever before.

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