ARTIFICIAL INTELLIGENCE AI semiconductors: The talk of the hardware industry

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Traditional chips have limited processing capabilities to support AI applications in terms of logic operations and memory allocation. Hence, AI semiconductors fill the “hardware” gap to provide faster solutions to AI-based applications. The article explains what AI semiconductors are, why are they important, and the latest news about them.

What are AI semiconductors?

An AI semiconductor, commonly called an AI chip, is an integrated circuit (or chip) that offers processing capabilities to AI, deep neural networks, robotics, and machine learning applications. In simple words, an AI semiconductor is a specialized hardware that facilitates the operation of the systems integrated with AI workflows.

An AI semiconductor chip can perform logic operations and memory functions for AI applications with increased efficiency. AI semiconductors are commonly manufactured with open-standard RISC-V (Reduced Instruction Set) architecture. The production of RISC-V AI chips is expected to increase as it offers flexibility, and customization options to AI applications.

Why are AI semiconductors important?

The term “AI” commonly points to software that generates smart responses and performs tasks with reduced manual interference. Another example is a robot that carries out physical work for humans. In simple words, AI semiconductor is hardware that goes on to help software and robots perform such functions.

Traditional hardware (General-purpose chips) do not have enough processing power to implement AI algorithms and perform long computations. This is because each AI operation cycle needs large computations, and memory expansion, consumes a high amount of power, and generates a huge quantity of data.

Replacement of CPU?

The CPU (Central Processing Unit) is required to perform N number of operations in a short span for the AI applications, increasing the load. As a solution, the industry came up with these “AI chips” to unload complex tasks from the processor and support complex AI algorithms, frameworks, and functionalities.

AI semiconductors are gradually reducing the relevance of CPUs in AI-based applications. It is important to note that AI semiconductors offer high efficiency and processing speed. AI semiconductors perform parallel operations to the CPU instead of sequential operations. In real-world applications, AI semiconductors simultaneously carry out tasks to reduce CPU load.

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Latest News: AI semiconductors

AI semiconductors are produced by big Tech companies like Google, Meta, Microsoft, and IBM, and chip giants like NVIDIA. The New York Times reports that NVIDIA has a decade-long monopoly in AI chips, accounting for 70 % market share in worldwide total sales of AI chips. In terms of manufacturing, the AI chip market is further divided between TSMC (Taiwan Semiconductor Manufacturing Company) and Samsung. The two companies implement some of the most advanced fabrication technologies to manufacture AI semiconductors. However, NVIDIA is globally known for AI semiconductor GPUs in the world.

Recently, AI chips have become the interest of OpenAI’s Sam Altman. According to the latest buzz, OpenAI has agreed to buy AI worth USD51 million from a start-up called Rain AI. Surprisingly, Rain AI is a start-up backed by Sam Altman through personal investments.

In comparison to other types of ICs, AI semiconductors hold a strong position in the chip industry. A Gartner report predicts that the AI semiconductor market will hit USD67.1 billion by the end of 2024 and nearly double USD119.4 billion in 2027.

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Semiconductors: A Comprehensive Guide

What are the applications of AI chips?

AI workflows

As discussed above, the main function of AI semiconductors is to support AI-based frameworks in AI, robotics, deep neural networks, and machine learning applications. Simply put, an AI semiconductor is the hardware for the processor that performs complex AI operations a thousand times faster than CPUs.

Datacenter accelerators

AI semiconductors are the hardware that functions as accelerators for the CPU. In large data centers, the CPU is loaded with a large number of continuous tasks. Multiple accelerators are deployed across the enterprise to unload the main processor for AI networking-based tasks and memory expansion.

GPU and TPU

AI semiconductors are hardware in GPU and TPU. It is used to build a GPU (Graphics Processing Unit) that performs various computations in parallel for AI-based systems. A TPU (Tensor Processing Unit) is a custom-designed accelerator by Google to speed up machine learning applications.

Industrial applications

Besides GPU and TPU, AI chips are used in FPGAs (Field Programmable Gated Arrays), and ASICs (Application Specific Integrated Circuits). AI semiconductors have applications in various industries such as IT, R&D, telecommunications, autonomous vehicles, consumer electronics, edge computing, image processing, healthcare, video gaming, and many more.

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