How are AI chips different from traditional chips? Discuss the importance of AI chips in various applications.
AI chips are built with specific architecture and have integrated AI acceleration to support deep learning-based applications.
Deep learning, 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.
Are they different 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.
- AI chips, essentially, reimagine traditional chips’ architecture, enabling smart devices to perform sophisticated deep learning tasks such as object detection and segmentation in real-time, with minimal power consumption.
Significance of AI chips
- Faster Computation: In order to execute advanced training models and algorithms, artificial intelligence applications often demand parallel processing capabilities.
- When compared to typical semiconductor devices at similar price ranges, AI hardware delivers higher parallel processing capabilities, which is expected to have up to 10 times more competing power in ANN applications.
- High Bandwidth Memory: It is predicted that specialised AI hardware will dedicate 4-5 times more bandwidth than regular CPUs. This is required because AI applications demand much higher bandwidth between processors for optimum performance due to the necessity for parallel processing.
- Natural language processing (NLP), computer vision, robotics, and network security are among the AI applications used in a number of industries, including automotive, IT, healthcare, and retail.
How to structure
- Give an intro about semiconductor chips
- Give the difference between the two
- Discuss the importance of AI chips and explain where its used
- Link to government schemes
- Write way forward