In the burgeoning world of information technology and computers, the adoption of Artificial Intelligence (AI) chips has surged recently. Different chipmakers are designing varied types of AI chips to power a wide range of AI applications. The article focuses on describing AI chips, their functionalities, types, applications, and significance.
About AI Chips
AI chips possess unique architecture and integrated AI acceleration designed to support applications based on deep learning. Active Neural Network (ANN) or Deep Neural Network (DNN), which falls under AI’s broad umbrella, is more commonly referred to as deep learning. It combines a series of computer commands or algorithms that stimulate brain activity and structure.
Functions of AI Chips
Deep Neural Networks learn new capabilities from existing data during a training phase. They then apply these learned capabilities to make predictions against previously unseen data, a process known as inference. This process of collecting, analyzing, and interpreting massive amounts of data becomes faster and easier with deep learning.
AI chips, with their hardware architectures, complementary packaging, memory, storage, and interconnect solutions, enable the integration of AI into diverse applications. These chips facilitate the conversion of raw data into useful information and eventually knowledge.
Types of AI Chips
Chipmakers design various AI chips for diverse AI applications. These include Application-Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), Central Processing Units (CPUs), and Graphics Processing Units (GPUs).
Applications of AI Chips
AI applications span across several sectors, including automotive, IT, healthcare, and retail. These applications involve Natural Language Processing (NLP), computer vision, robotics, and network security.
Reasons for the Rise in AI Chips
The surge in the adoption of AI chips in data centers significantly contributes to the market’s growth. Additionally, the rising need for smart homes and cities and surge in investments in AI startups are driving the global AI chip market. This market accounted for approximately USD 8 billion in 2020 and is expected to hit USD 195 billion by 2030, growing at a Compound Annual Growth Rate (CAGR) of 37.4% from 2021 to 2030.
Significance of AI Chips over General Purpose Hardware
AI applications typically require parallel computational capabilities for running sophisticated training models and algorithms, which AI hardware provides. AI hardware offers up to 10 times more computational power in ANN applications compared to traditional semiconductor devices at similar price points. Also, specialized AI hardware allocates 4-5 times more bandwidth than traditional chips, catering to the high bandwidth requirements of AI applications due to the need for parallel processing.
AI in UPSC Civil Services Examination
In the recent UPSC Civil Services Examination, AI was a topic of focus. Previous year questions included assessing AI’s capabilities in tasks like reducing electricity consumption, creating meaningful short stories and songs, diagnosing diseases, text-to-speech conversion, and wireless transmission of electrical energy. The answer revealed that AI could bring down energy consumption, diagnose diseases, and convert text to speech effectively but fell short in creating artistic content like meaningful short stories and songs and transmitting electrical energy wirelessly.
Last Modified: February 15, 2024