AI chips are built with specific architecture and have integrated AI acceleration to support deep learning-based applications.
- AI chips help turn data into information and then into knowledge.
- The Worldwide AI chip industry accounted for $8.02 billion in 2020
Difference from Traditional Chips:
- When traditional chips, containing processor cores and memory, perform computational tasks, they continuously move commands and data between the two hardware components.
- These chips, however, are not ideal for AI applications as they would not be able to handle higher computational necessities of AI workloads which have huge volumes of data.
- Although, some of the higher-end traditional chips may be able to process certain AI applications.
Deep Learning: It is more commonly known as active neural network (ANN) or deep neural network (DNN), is a subset of machine learning and comes under the broader umbrella of AI.
It combines a series of computer commands or algorithms that stimulate activity and brain structure.
DNNs go through a training phase, learning new capabilities from existing data.
DNNs can then infer, by applying these capabilities learned during deep learning training to make predictions against previously unseen data.
Deep learning can make the process of collecting, analysing, and interpreting enormous amounts of data faster and easier.
- Computer vision: Some of these chips support in-vehicle computers to run state-of-the-art AI applications more efficiently.
- Robotics: AI chips are also powering applications of computational imaging in wearable electronics, drones, and robots.
- Natural language processing (NLP): The use of AI chips for NLP applications has increased due to the rise in demand for chatbots and online channels such as Messenger, Slack, and others.
- They use NLP to analyse user messages and conversational logic.
- Used for network security across a wide variety of sectors, including automotive, IT, healthcare, and retail.
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