What Are AI Chips Used For? Main Functions You Should Know

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The Artificial Intelligence chips (AI chips) are specialized processing units  designed to handle the AI workloads. They are neural processing units that speed up the learning and execution of the AI algorithms.

AI chips have numerous features that separate them from the ordinary computer chips. For example, they have high data bandwidth, a feature that makes them capable of handling and processing large volumes of data. They also have the highest level of parallelism, a  reason behind their fast processing speed.

So, what are these chips used for?  Read on as we outline and discuss the main functions of AI chips.

Training neural networks

Deep neural networks are integral components of the artificial intelligence. They need to learn and be trained on what to do based on the target goals of the developers. It is the cire function of the chips to implement the training of these neural networks.

The training process entails feeding the neural network with large volumes of data about an area or target subject. The re is also some heavy computation involved in trying to test  the output of the network vs the error it generates. This error is then propagated backwards the layers of the neural network and necessary adjustments made. This is done severally to improve the accuracy of the neural network.

All these complex computations are done by the AI chips. Remember that the chips are designed to be fast and efficient hence capable of doing these operations. The parallelism feature of the chips give  them an upper hand in executing numerous calculations at ago. It also makes it possible to shorten the training time for the deep neural network whereby models are improved and the necessary changes made.

Inference of models and networks

Model inferencing is another key function of the AI chips. It is all about ensuring that a network or even an AI model can make certain predictions based on the given input data. During the inference training the output of the model is basically the probability distribution of the predicted or expected outputs.

Inferencing is slightly similar to the training only that here backpropagation algorithm is deployed against the fixed weights of the network.

So, where does the AI chip comes in during the inferencing?

First, it is important to note that inferencing of a model requires handling and processing of large volumes of data. In most  application, inferencing requires real-time data processing and  this kind of task can be done by a specialized AI processor.

Also, the fact that AI chips are embedded with specialized circuits  enhance the speed  and accuracy of complex computations during the inferencing process. The time saved by these chips allow machine learning engineers to develop smart systems that can process and execute data in the real-time. This is a critical function in most AI fields such as autonomous vehicles and even in medical industry.

Natural Language Processing (NLP)

Artificial intelligence chips are at the core of the NLP function by the AI systems. The chips speed up the AI systems process compute and understand the human language. As  subset of AI, Natural Language Processing  encompasses many operations such as speech recognition, parsing, entity recognition, speech tagging among others. AI chips have robust features that can perform these functions at a fast rate.

The vast computational resources  in the AI chips speed up the learning rate of different human language based on the datasets used for training. These chips make it possible for the algorithms to learn the words used in a  language, predict and generate the sequence of the words. The chips are also capable of identifying sentiments of a language and processing it.

Robotics

Robotics is another major application area of artificial intelligence. We are witnessing the development of  smart robots and this is due to the development of the AI chips. These  robots utilize the massive computational power of the AI chips to perform human-like actions.

The immense data processing of the chips ensure that the robots can perceive their environment and execute tasks  depending on the given input. The robots accept input data from their sensors and cameras then use their deep neural network to act accordingly.

What about the timely reaction of the intelligent robots? It is all because of the powerful AI chips. The chips control the motion and movement of the intelligent robots ensuring that they react as desired and in perfect time.

In overall, the effectiveness of the chips is the core reason why robotics is used in a wide range of industries ranging from health, manufacturing and even security systems.

Computer vision

As the name suggests, this is all about giving computers ‘eyes’ so that they can see images, texts and videos. After seeing, the computer should be able to analyze and interpret the vision. This is possible with the help of AI chips.

Computer vision comprises of various processes that a normal chip cannot do. They include image processing, image analysis, object isolation, video detection, data analysis among others. AI chips have the capacity to implement all these operations within a small time-frame.

The parallel processing feature of the AI chips further make it possible to implement computer vision. Also, the chips are designed to train complex machine learning algorithms which are used in object detection and isolation. It is through such neural network training that computer vision is able to be implemented with the highest degree of accuracy.

Conclusion

AI chips are part and parcel of every AI process. They are used in all the applications and fields of artificial intelligence. Technically, they are  irreplaceable. You cannot replace an AI chip with an ordinary chip if at all you are working on an AI system. Also, the chip should meet the minimum standards for that specific application area.

If you want to buy AI chips, ensure that you get from the right manufacturers and suppliers. For instance, we at ICRFQ are a reputable distributor of AI chips in China. Contact us and we will guide you in choosing the right chip.

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