Artificial Intelligence (AI) Chips

Syllabus: General Studies Paper 3

The adoption of Artificial Intelligence (AI) chips has risen, with chipmakers designing different types of these chips to power AI applications.

What are AI chips?

• AI chips are built with specific architecture and have integrated AI acceleration to support deep learning-based applications.
• These chips, with their hardware architectures and complementary packaging, memory, storage and interconnect technologies, make it possible to infuse AI into a broad spectrum of applications to help turn data into information and then into knowledge
• 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, designed for diverse AI applications.

How 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.
• Although, some of the higher-end traditional chips may be able to process certain AI applications
• 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.

What are their applications?

• AI chips are used for a multitude of smart machines and devices, including ones that are said to deliver the performance of a data centre-class computer to edge devices.
• Some of these chips support in-vehicle computers to run state-of-the-art AI applications more efficiently.
• AI chips are also powering applications of computational imaging in wearable electronics, drones, and robots.
• The use of AI chips for NLP (Natural Language Processing) 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.