The rise of AI chips

Artificial Intelligence or AI Chips

  • AI chips are the ones that are made with specific architecture and are integrated with Artificial Intelligence (AI) acceleration to assist in deep learning-based applications. 
    • Deep learning or Active Neural Network (ANN) or Deep Neural Network (DNN) is a division under machine learning and also comes under the AI framework.
    • It includes a series of computer algorithms that result in activity and brain structure. Deep learning new capabilities through training from existing data. 
    • Deep learning has the ability to apply these capabilities learned during the training phase and make predictions about unseen data. 
    • Deep learning thus facilitates the process of collecting, analysing, and interpreting large amounts of data in a faster and simpler manner.
  • AI chips with advanced hardware design with advanced packaging, memory, storage help to extend AI to various applications which turns data into information and then into knowledge. 
  • The different types of AI chips include Application-Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs) and Central Processing Units (CPUs) which are designed for different applications.

AI Chips v/s Traditional Chips

  • The traditional chips perform various tasks by continuously moving commands and data between the hardware components. 
    • These chips are not suitable for AI applications since they cannot handle advanced computational processes and AI workloads which deal with large amounts of data. 
  • However, AI chips are equipped with processor cores and other AI-optimised cores that are made to perform computational tasks harmoniously. The AI cores are enhanced to address the demands of AI workloads with low-latency and are integrated with other processor cores that are intended to handle non-AI applications as well.
    • AI chips are basically advanced chips that enable smart devices to perform complex deep learning tasks like object detection and segmentation in real-time, with fewer power requirements. 

Applications of AI Chips

  • AI chips are used in smart machines and devices such as data centre-class computers and edge devices. 
  • AI chips are used to assist in-vehicle computers to perform AI applications more efficiently. 
  • AI chips are used in wearable electronics, drones, and robots.
  • AI chips are also being used in Natural language processing (NLP) applications whose demand has increased due to the extensive usage of chatbots and online channels such as Messenger, Slack, and others. 
  • AI chips have also been deployed in banking, finance, trading, and insurance sectors as they help in customer interaction.

Recent developments

  • One of the major firms, Nvidia recently unveiled its H100 GPU (graphics processing unit), which is said to be the world’s largest and most powerful AI accelerators and the makers claim that it can sustain traffic equivalent to the entire world’s internet traffic.
    • Nvidia also has a wide portfolio of AI chips such as Grace CPU and A100 GPUs, that are capable of handling some of the largest AI models 
  • Intel Corporation recently launched new AI chips with deep learning capabilities.
    • Intel’s Habana Labs launched its second-generation deep learning processors named Gaudi2 and Greco. 
  • IBM is also working on AI chips that can perform financial services tasks such as fraud detection, loan processing, clearing and settlement of trades, anti-money laundering and risk analysis.

Path ahead

  • The Cerebras Systems, which is one of the manufacturers of AI chips, has set a new benchmark with its brain-scale AI solution, it is equipped with CS-2 powered by the Wafer Scale Engine (WSE-2) with 2.6 trillion transistors and 8,50,000 AI optimised cores. 
    • The company said that the human brain consists of about 100 trillion synapses, and a single CS-2 accelerator can support models of over 120 trillion parameters (synapse equivalents) in size.
  • Neuromorphic computing is another advanced design approach that employs an engineering method based on the activity of the biological brain.
    • Extensive usage of neuromorphic chips is expected in the automotive industry in the coming years.
  • There is an increase in the demand for smart homes and cities,  which has resulted in increased investments in AI start-ups boosting growth in the AI Chip market.
  • The global AI chip industry accounted for $8.02 billion in 2020 and is estimated to cross $194.9 billion by 2030 achieving significant growth.

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