The Rise of AI Chips : Daily Current Affairs

Relevance: GS-3: Science and Technology- developments and their applications and effects in everyday life.

Key Phrases: Artificial Intelligence, deep neural network, H100 GPU, ASICs, Non-AI applications, Specialised AI chips, FPGAs, Neuromorphic Chips.

Why in News?

Market leader Nvidia recently announced its H100 GPU (graphics processing unit), which is said to be one of the world’s largest and most powerful AI accelerators, packed with 80 billion transistors.
The adoption of Artificial Intelligence (AI) chips has risen, with chipmakers designing different types of these chips to power AI applications such as natural language processing (NLP).
The increasing adoption of AI chips in data centres is one of the major factors driving the growth of the market.

What are AI chips?

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.
There are different types of AI chips such as:
- Application-specific integrated circuits (ASICs),
- Field-programmable gate arrays (FPGAs),
- Central processing units (CPUs) and GPUs,

How are AI chips 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 are not ideal for AI applications as they would not be able to handle higher computational necessities of AI workloads that have huge volumes of data.
In comparison, AI chips generally contain processor cores as well as several AI-optimised cores (depending on the scale of the chip) that are designed to work in harmony when performing computational tasks.
The AI cores are optimised for the demands of heterogeneous enterprise-class AI workloads with low-latency inferencing, due to close integration with the other processor cores, which are designed to handle non-AI applications.

AI Applications:

Banking
- A number of banks have already adopted AI-based systems to provide customer support, detect anomalies and credit card frauds. An example of this is HDFC Bank.
Agriculture
- Now a day's agriculture is becoming digital, and AI is emerging in this field. Agriculture is applying AI as agriculture robotics, solid and crop monitoring, and predictive analysis. AI in agriculture can be very helpful for farmers.
Astronomy
- Artificial Intelligence can be very useful to solve complex universe problems. AI technology can be helpful for understanding the universe such as how it works, its origin, etc.
Healthcare
- Healthcare Industries are applying AI to make a better and faster diagnosis than humans.
Gaming
- AI can be used for gaming purposes. The AI machines can play strategic games like chess, where the machine needs to think of a large number of possible places.
Finance
- AI and finance industries are the best matches for each other. The finance industry is implementing automation, chatbot, adaptive intelligence, algorithm trading, and machine learning into financial processes.
Data Security
- The security of data is crucial for every company and cyber-attacks are growing very rapidly in the digital world. AI can be used to make your data safer and more secure.
AI in Social Media
- Social Media sites such as Facebook, Twitter, and Snapchat contain billions of user profiles, which need to be stored and managed in a very efficient way.
- AI can organize and manage massive amounts of data.

Do you Know?

Nvidia Corporation, Intel Corporation, IBM Corporation, Advanced Micro Devices, Alphabet Inc., Samsung Electronics Co., Ltd, Qualcomm Technologies, Inc., and Apple Inc. are some of the key players in the AI chip market.
Nvidia, which dominates the market, offers a wide portfolio of AI chips including Grace CPU, H100, and its predecessor A100 GPUs, capable of handling some of the largest AI models with billions of parameters.
The company claims that twenty H100 GPUs can sustain the equivalent of the entire world’s internet traffic.

Future Projects:

An increase in the adoption of neuromorphic chips in the automotive industry is expected in the next few years, according to Research And Markets.
Additionally, the rise in the need for smart homes and cities, and the surge in investments in AI start-ups are expected to drive the growth of the global AI chip market, as per a report by Allied Market Research.
The Worldwide AI chip industry accounted for $8.02 billion in 2020 and is expected to reach $194.9 billion by 2030, growing at a compound annual growth rate (CAGR) of 37.4% from 2021 to 2030.

Note:-

A neuromorphic chip is an analog data processor inspired by the biological brain.
The word neuromorphic itself derives from the words neuro, which means "relating to nerves or the nervous system," and morphic, which means "having the shape, form or structure”.

Conclusion:

To better prepare for the future society in which artificial intelligence (AI) will have a much more pervasive influence on our lives, a better understanding of the difference between AI and human intelligence is necessary.
Now the great challenge of AI is to find ways of representing the commonsense knowledge and experience that enable people to carry out everyday activities such as holding a wide-ranging conversation, or finding their way along a busy street.

Sources: The Hindu

Mains Question:

Q. What are the different ways in which AI chips are being used in various sectors? What are the recent innovations in the industry?