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AI Processors in Microprocessors
Artificial Intelligence is one of the key technologies behind the modern revolutionization of data processing, computing industries, and many other fields of engineering and technology. It empowers digital machines to make decisions and think like human beings. Today, processor developers and manufacturers are integrating artificial intelligence into microprocessors, and these processors are referred to as AI Processors or AI Chips.
We can define an AI Processor as a device that can perform huge amount of data processing or computing tasks with simulating human intelligence in the machine.
This chapter will provide a basic introduction to Artificial Intelligence Processors, their major components, advantages, and key applications. So, let's start with a basic overview of AI processors.
What is an AI Processor?
A microprocessor which is specially design and developed to efficiently perform tasks related to artificial intelligence which an ordinary microprocessor cannot do, is referred to as an AI Processor.
A typical AI processor is integrated with various tools that can handle AI workloads, and these can be GPU (Graphical Processing Unit), TPU (Tensor Processing Unit), NPU (Neural Processing Unit), and many more.
We can state that an AI processor is an advanced, efficient, and more powerful version of an ordinary microprocessor which provides capabilities to manage high-end tasks such as machine learning, computer vision, generative AI, and much more.
Let's now understand the role of AI processors in real world.
What is the Role of AI Processors in Real World?
Today, AI processors are integral parts of all AI-based computing systems or servers. In other words, it can be said that AI processors are the fundamental building blocks of all AI powered devices like embedded systems, IoT devices, and many more. In real world applications, AI processors help performing highly complex computations such as multiplication of multi-dimensional matrices. Along with this, they also help in a variety of applications as −
- Machine learning
- Deep learning
- Advanced data analytics and trend predictions
- Natural language processing
- Computer vision and graphics
- Generative AI, any many other
From this discussion, we can clearly observe that the AI processors play a significant role in the real world, from data related applications to training and optimization.
Features of AI Processors
As we discussed above, AI processors are specially designed processing devices to perform AI based jobs or tasks. For this purpose, they have specialized processing unit, optimized memory architecture, power efficient design, etc.
Some of the key features of AI processors are listed below −
- AI processors use a specialized processing unit which is optimized for AI related tasks.
- As AI based tasks require efficient memory access and higher bandwidth to process large amounts of data. Thus, they must have a highly optimized memory architecture.
- AI processors are designed to integrate in power-constrained environments like mobile devices, edge devices, smartphones, and others. Hence, they must have power efficient design.
- AI algorithms generally perform multiple calculations at the same time. Thus, AI processors must support parallel processing to allow concurrent computations, for providing faster processing speed.
- AI processors use low-precision arithmetic like 8-bit or 16-bit instead of higher precisions like 32- or 64-bits which are commonly used in traditional processors.
- Another key feature of AI processors is that they have dedicated neural network accelerators to efficiently execute AI-related tasks.
- AI processors are capable in evolving with new data and changing environments.
Advantages of AI Processors
The benefits of AI processors depend on their design, architecture, and hardware type used. Listed below are some major advantages of AI processors −
- AI processors are more efficient in performing complex tasks.
- AI processors can integrate with other processing units like CPUs or GPUs to improve performance of overall system for performing complex AI related tasks.
- AI processors are compatible to integrate with FPGAs (Field Programmable Gate Arrays) to provide higher power efficiency and flexibility in various applications.
- AI processors, if selected properly, can result in reduced cost, higher reliability, and increased return on investment.
Applications of AI Processors
In modern days, AI processors are widely being used in a significant range of applications, some common examples of usage of AI processors are listed here −
- AI processors are used in parallel computing applications to handle large sets of instructions. They are widely used in large language models such as BERT, ChatGPT, and other Generative AI tools.
- AI processors are also used in autonomous vehicles to process sensor data to provide real-time navigation and decision-making.
- AI processors are also used in low-power devices where power and thermal restrictions limit the use of GPUs.
- Intelligent robots also make use of AI processors to enable real-time decision-making in industrial processes.
- AI processors are also engaged for solving complex scientific problems involving large datasets.
Conclusion
In this chapter, we covered the fundamentals of AI processors. We can conclude that AI processors are employed in various applications such as data centers, robotics, edge computing, and many more to improve performance, efficiency, and scalability.