Last Updated on October 27, 2023 by Kevin Chen
Image source Analytics Insight
With the growing popularity of AI technology, there is need to build hardware products that will meet the needs of artificial intelligence and machine learning. It is impossible to talk about AI hardware systems without mentioning chips or integrated circuits.
Artificial Intelligence (AI) chips are special computer chips designed and built to perform the artificial intelligence tasks in the most accurate ways possible. These chips have special features that make them process and optimize large data sets while at the same time run complex algorithms that are used in major AI projects.
These are not your ordinary electronic chips. Specialized hardware technologies such as tensor processing units (TPUs) are used to create these chips. This gives the chips enhanced computation capability. All other aspects of computer such as speed, graphics and audio are considered when designing the AI chips.
Are you a researcher and you plan to build an AI system? Or, are you a manufacturer specializing on AI hardware projects? Here is a perfect guide for you. This article will highlight all you should know about artificial intelligence chips. We will also give you the best tips for buying AI tips suitable for your project.
What is the difference between AI chips and traditional chips?
While both are chips, the difference is AI chips are more specialized for handling large volumes of data required for training and fine-tuning the AI applications. On the other hand, traditional chips are designed for the general computing tasks.
There are other detailed differences between these two types of chips. For example, the processing architecture of AI chips is more suitable for the AI workloads unlike the traditional chips whose architecture is ideal for the general workloads.
Generally, traditional chips tend to have higher power consumption than the AI chips. This is despite the fact that AI chips do heavier tasks.
In overall, artificial intelligence chips are more of laser-focused while the traditional chips are for general application.
What are the functions of AI chips
Just like other types of chips, AI chips have specific roles to play in devices and systems. Let’s look at the main functions of these artificial intelligence chips
Data processing
Data is a major component of the AI systems. Data processing is the primary function of the AI chips. They are designed to handle and process large sets of data that will be used for training and perfecting the algorithms.
A normal chip may not have the capability to deal with such volumes of data and this is why you should get a specialized chip. When it comes to data processing, speed gives these chips an extra edge. They can handle complex arithmetic and logical functions on large data sets within seconds. The aspect of data processing is highlighted on the ability of the chips to handle data in different formats; including the highly graphical data.
Power optimization
Functions and operations related to AI and ML are quite power-hungry. AI chips are tasked with the role of enhancing power efficiency and optimization in the systems. They ensure that the systems run without wastage of power hence minimizing the overall cost of running the systems.
There are various ways in which the artificial intelligence chips enhance power optimization and efficiency. One basic way is by ensuring that the hardware systems consume less power while processing data in the fastest way possible. The speed at which data is processed translates to less running time hence less power being consumed.
Also, these chips come with advanced power management features such as dynamic voltage frequency which adjust the power consumption based on the workload. The fact that they are specialized chips also means that they are more dedicated to AI tasks hence cutting down on the number of processing cycles to be executed. This goes a long way in reducing power consumption.
Algorithm acceleration
This is another key function of the AI chips. They speed up the algorithms that run the AI systems and applications. You will be surprised by the insane processing power of the algorithms used in AI systems. Normal chips are not capable handling them.
The acceleration ensures that you get faster results that will be used for making the models. It is a critical aspect in many different AI fields such as natural language processing, autonomous systems, speech recognition, image processing among others. It is a recipe for the fast learning and decision making.
Memory management
Other than data processing, AI chips also perform the role of memory management in their respective systems and applications. Memory management is all about the proper storage and handling of the memory that is used in training the algorithm.
As we have already mentioned, large volumes of data and normal chips may not have the capacity to guarantee smooth and efficient management. The chips have special features such as high-bandwidth memory (HBM) which is responsible for optimizing power used in moving data from the CPU to the memory.
The chips have a robust cache management system designed to optimize memory efficiency while at the same time reduce data latency. Keep in mind that proper memory management goes a long way in improving efficiency of AI systems.
Sensor integration
Most AI-based hardware systems are embedded with sensors whose purpose is to collect data. These sensors include cameras, data, biometric systems among others. Artificial intelligence chips guarantee proper integration on these sensors with other components. For example, they facilitate data pipeline between the sensors and the data processing and storage.
Still on the sensors integration, the chips ensure that sensors are integrated with other specialized interfaces. For instance, in autonomous vehicles, the chips ensure that sensors that monitor the car send real-time data to the vehicle.
Neural Network Inference
First, neural network inference refers to the process in which a n AI system uses a trained network to predict the expected outcome. The network will use data that has been fed into the algorithm to make such predictions.
In this case, the primary role of the AI is to speed up the neural network inference. This is a complex and data-intensive task that a normal chip wont be able to execute. Special features embedded in the AI chips make it possible to do complex computations required for the neural network inference.
Basically, those are the critical functions of AI chips. Most of these chips are able to do execute the functions of normal chips only that their extra design , features and architecture give them an upper hand.
Features and characteristics of AI chips
Image source IEEE computer society
How are these chips able to execute the functions we have just listed? Well, there are a number of features that make it possible and let’s discuss some of them.
Parallel processing
This is a common feature in most advanced computers. It simply implies that the chips are able to execute multiple tasks or computations at the same time using different embedded processing units. This feature ensures that the chip will divide the workload into different cores and each core will work on a designated task.
The main advantage of parallel processing by AI chips is speed. It will compute complex task within a short time.
High speed memory
AI chips are capable of handling large volume of data thanks to their high-speed memory system. Most of these chips come with dedicated on-chip memory that are specifically designed for speed. Also, the high-bandwidth memory feature facilitates fast data-retrieval by the chip.
Low power consumption
It is obvious that AI applications are heavy consumers of power. The good thing is there has been advancement in this technology that has led to the development of energy-efficient AI chips. They are still capable of achieving high performance while saving on the energy usage.
Customizable architecture
These chips give room for maximum flexibility. They can be easily customized to meet the needs of different applications. Their flexibility also make it possible for the AI technology to be implemented in different application areas.
The flexibility of AI chips is further enhanced by their ability to support multiple frameworks. This means that developers can use different platforms to work on a single AI project.
Integration with cloud platforms
In the era of cloud computing, there is minimal reliance on the on-premise storage. To take care of this, we have advanced AI chips that are integrated with cloud computing platforms. This type of integration makes it possible for the chips to handle data management and even do data processing and storage. The integration also makes the hardware and AI systems accessible to developers in different locations.
Tensor processing units
This is a unique feature of AI chips. They are embedded with tensor processing units (TPUs). The main function of these units is to speed up the matrix operations that are responsible for running the deep learning algorithms.
On-chip optimization
This refers to the art of incorporating specialized hardware and other features so as to optimized the performance of a chip. AI chips feature this optimization so that they can deliver the best performance in the area of artificial intelligence and machine learning. These forms of optimization include clock speed optimization, dynamic voltage and frequency scaling (DVFS) among others.
Scalability
Other than being highly flexible, AI chips are designed for scalability. This feature allows manufacturers to expand the powers and capabilities of the chips to meet the new demand. For example, the data handling of the chip can be expanded to increase the capacity of the system for new data sets. Scalability also refers to the process of increasing the speed of the AI systems.
Types of AI Chips
There are different types of AI chips in the market. These types include:
-ASIC(Application-Specific Integrated Circuit) As the name suggests, these chips are designed to perform specific AI-related functions. For example, you can buy an ASIC for image recognition, one for deep learning among other functions. The fact that these are highly specialized chips means you should expect the best performance. They are also highly efficient even though they come at high cost.
-GPU (Graphics Processing Unit): Artificial Intelligence GPU chips are designed for graphical processing. Even though we have normal chips for graphics, these ones are more AI-specialized. They have powerful capabilities of processing graphical data. Like most AI chips, they also come with parallel processing feature. They are ideal for performing AI functions such as deep learning.
-NPU(Neural Processing Unit): NPU chips are mainly designed for neural network processing. In the field of AI, a neural network refers to an algorithm that works like a human brain. The network comprises of interconnected nodes that send data to each other. NPU chip performs functions such as image recognition, voice recognition and natural language processing (NLP). The chip is also good at power optimization.
-FPGA (Field-Programmable Gate Array): These are also specialize chips designed for specific AI functions. They are easily customizable and highly scalable.
-TPU (Tensor Processing Unit): Designed and developed by Google, these chips are mainly used for handling machine learning and deep learning workloads. They are specifically used with the TensorFlow AI framework whereby they optimize the performance of the AI applications.
CPU (Central Processing Unit): Even though these are general-purpose chips, you can find some that are optimized for the AI applications. You only have to check the specs of this CPU to separate it from the normal ones.
As you can see, each type is suited for a specific function and application area. Before buying an AI chip, analyze your application area and determine whether the chip will be able to meet your needs.
What are the applications of AI chips?
As the world is moving more towards the age of artificial intelligence, you should expect to see AI chips everywhere. Some of the applications in which AI chips are heavily utilized include:
- Natural language processing
- Robotics
- Automotive such as self-driving vehicles
- Computer vision
- Network security
We expect to see more applications in the coming years
Conclusion
Now you know all the basics and the details of the AI chips. If you are planning to get into this field, you know where to start from. And if you are planning to buy AI chips, ensure that you get from reputable AI chips suppliers and manufacturers. This way, you can be sure of getting high-quality products.
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