Artificial Intelligence (AI) Chips

In News

• Recently, Nvidia’s rival Intel launched new Artificial Intelligence (AI) chips to provide customers with deep learning compute choices for training and inferencing in data centres. 

AI Chips

• 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 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.
• The increasing adoption of AI chips is one of the major factors driving the growth of the market.
• Types of AI chips:
  • 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
• 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. 
• Function: 
  • 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. 

Applications 

• 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.
• AI processors with on-chip hardware acceleration are designed to help customers achieve business insights at scale across banking, finance, trading, insurance applications and customer interactions. 

Significance

• Deep learning can make the process of collecting, analysing, and interpreting enormous amounts of data faster and easier.
• 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.

Recent Initiatives by Firms

• Market leader Nvidia recently announced its H100 GPU (graphics processing unit). 
  • It is one of the world’s largest and most powerful AI accelerators, packed with 80 billion transistors.
• Intel’s Habana Labs launched its second-generation deep learning processors — Gaudi2 and Greco.
• IBM’s new AI chip can support financial services workloads like fraud detection, loan processing, clearing and settlement of trades, anti-money laundering and risk analysis.

Way Ahead

• An increase in the adoption of neuromorphic chips in the automotive industry is expected in the next few years.
• 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. 
• India should also focus on increasing its AI research to utilise more of its applications.

Source:TH