AI Chips

Context:

The U.S. unveiled new export rules on advanced computing chips used for artificial intelligence, aiming to facilitate sales to allied nations and further curb access to countries like China. The new rules update controls on chips, requiring authorisations for exports, re-exports and in-country transfers. 

What are AI chips?

• About
  • AI chips are designed with specific architecture and have integrated AI acceleration to support deep learning-based applications.
  • Deep learning, commonly known as Active Neural Network (ANN) or Deep Neural Network (DNN), is a subset of machine learning and falls under the broad umbrella of AI.
• Functions
  • It combines a series of computer commands or algorithms that stimulate activity and brain structure.
  • DNNs have a training phase wherein it learns new capabilities from existing data.
  • DNNs can then inference, 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 vast amounts of data much faster and easier.
  • Such chips, by their hardware architecture, complementary packaging, memory storage, and interconnect solutions for AI, offer the possibility for the integration of AI in every kind of spectrum of applications through which data would be turned into information and from there into knowledge.
• Types of AI Chips
  • Designed for all kinds of divergent AI application
  • Application Specific Integrated Circuits (ASIC), Field Programmable Gate Arrays(FPGA), CPU and GPU.
• Applications
  • The AI applications have ranged from natural language processing to computer vision and robotics, from network security, in the vast automotive, IT, healthcare, and retail segments.
• Reason for the Growth
  • The main growth driver of this market is increased data centre uptake of AI chips.
    • A data center is a physical facility that stores, processes, and distributes data and applications for a business. 
  • Moreover, the increasing demand for smart homes and cities and the investment in AI start-ups is expected to fuel the growth of the global AI chip market.

Importance of AI Chips compared to General Purpose Hardware?

• Faster Computation
  • Artificial intelligence applications typically require parallel computational capabilities in order to run sophisticated training models and algorithms.
  • AI hardware provides more parallel processing capability that is estimated to have up to 10 times more competing power in ANN applications compared to traditional semiconductor devices at similar price points.
• High Bandwidth Memory
  • Specialized AI hardware is estimated to allocate 4-5 times more bandwidth than traditional chips.
  • This is because AI applications require much more bandwidth between processors due to the need for parallel processing.