Semiconductor is the core of the intelligent era and is very important for the development of artificial intelligence. They improve the speed of data processing, greatly expand the storage capacity, and make it possible for complex algorithms to run. With the development of technology, semiconductor chips are being more and more miniaturized, and the power efficiency is constantly improving, which meets the dual requirements of artificial intelligence for computing power and energy efficiency. In addition, the discovery of new materials and the innovation of production technology have promoted the leap of semiconductor performance and further promoted the wide application and development of artificial intelligence technology.
Deep correlation between artificial intelligence and semiconductors
Relationship between basic requirements of artificial intelligence and semiconductors
The basic requirements of artificial intelligence (AI) involve the ability of fast processing and pattern recognition of a large amount of data, which directly depends on the computing power and storage capacity of semiconductor technology. High performance computing chips, such as GPU and ASIC, provide necessary computing support for complex AI algorithms. For example, a single NVIDIA Tesla V100 GPU can achieve floating-point performance of 14 teraflops, while its power consumption is 250 watts, showing high processing speed and energy efficiency ratio. The market price of this GPU is about $8,000, which needs to be included in the total budget when deploying large-scale data centers.
In the early stage of AI development, the algorithm model is small, and the demand for computing power can be met by traditional CPU. However, with the continuous increase of deep learning model, the required parameter level has increased from millions to billions. Correspondingly, semiconductor chips must provide higher processing speed and larger storage capacity. A deep learning model may need hundreds of GB of memory to store temporary data, while the price of ordinary DDR4 memory chips in the market is about $70 per 16GB, and the highly capacity demand will lead to significant cost increase.
The role of semiconductor technology in the development of AI algorithm
The progress of semiconductor technology promotes the rapid development of AI algorithm, especially in the field of machine learning (ML). Advanced semiconductor materials, such as silicon germanium alloy, improve the switching speed of transistors, thus improving the overall processing speed and efficiency of chips. A processor using silicon-germanium alloy can achieve a higher clock frequency, which can exceed 5 GHz, while the clock speed of ordinary silicon-based processors is usually below 3 GHz.
The Development History of AI Hardware and the Promoting Role of Semiconductor Technology
The development history of AI hardware can be traced back to the first generation of computers using vacuum tubes and modern integrated circuits (IC) using semiconductor materials. Taking the development of NVIDIA as an example, its early products mainly served the game market, while its latest AI processors such as Volta and Turing architecture have been widely used in AI and machine learning tasks. The manufacturing cost of Tesla V100 GPU based on Volta architecture is as high as 3,000 US dollars, but it provides unprecedented acceleration capability for deep learning, which is the irreplaceable role of semiconductor technology in the development of AI field.
Application of Semiconductor in Artificial Intelligence
Semiconductor Requirements for Cloud Computing and Data Centers
Cloud computing platform and data center are the main places for artificial intelligence computing, which require extremely high performance of semiconductors. Take the data center of cloud service providers such as Amazon AWS as an example, which uses customized semiconductor chips (such as Graviton2 processor) to provide higher processing power and energy efficiency ratio. The power design point (TDP) of Graviton 2 is 60 watts, which is 7 times higher than that of similar products. From the cost point of view, the investment in semiconductors is very high in the initial stage, and the construction and operation budget of data centers may be as high as billions of dollars. For example, the initial investment budget for building a medium-sized data center may be between $100 million and $200 million, of which semiconductor procurement costs account for 15% to 30% of the total cost.
Application of Semiconductor Technology in Edge Computing
As a distributed computing architecture, edge computing transfers data processing tasks from the cloud to local devices, which directly improves the speed of data processing. The applications of semiconductors in edge devices, such as smart phones and unmanned aerial vehicles, need to have the characteristics of high performance and low power consumption. The power consumption of SoC (System on a Chip) used by a typical smart phone is about 3 to 5 watts, and its processing speed requires at least 2 GHz to run AI applications smoothly. The cost of semiconductors has a significant impact on the total cost of edge devices. For example, the SoC cost of a high-end smart phone may reach more than 20% of the whole machine cost, that is, about 100 to 200 dollars.
The role of semiconductors in smart devices
In the field of smart devices, semiconductors not only need to support conventional computing tasks, but also meet the unique needs of AI, such as speech recognition and image processing. The market price of smart home devices, such as smart speakers, ranges from $30 to $200, and the power consumption of speech recognition chips is only about 1 watt, but it can support real-time processing of continuous speech input. The life of this chip is usually designed to be more than 5 years to meet the long-term use requirements.
Latest progress of semiconductor technology and its influence on AI
Research and Development of New Semiconductor Materials
In the research and development of new semiconductor materials, the third generations semiconductor materials such as silicon carbide (SiC) and gallium nitride (GaN) have shown better performance than silicon (Si). For example, the power density of gallium nitride is as high as 4000 W/cm2, far exceeding the traditional silicon material about 150 W/cm2, and the energy efficiency ratio is also significantly improved. Although the monolithic cost of gallium nitride is about 20% higher than that of silicon, its performance advantages in high frequency and high temperature applications make its value reflected, especially in 5G communication and electric vehicles.
Innovation and Breakthrough of Semiconductor Manufacturing Technology
In the manufacturing process, extreme ultraviolet (EUV) etching technology is gradually becoming the mainstream. For example, TSMC has realized mass production of 5 nm process, and plans to introduce more advanced 3 nm process in the future. This miniaturization technology allows more transistors to be integrated on the same chip, thus enhancing performance and reducing power consumption. 5 nanometer technology can reduce the power consumption of the chip by about 25% and increase the running speed by 20%. However, the cost of EUV equipment is extremely high, and the price of a machine is about 100 million to 150 million US dollars. Therefore, the promotion of EUV technology requires high financial strength of semiconductor manufacturers.
The latest development of semiconductor miniaturization technology
The breakthrough in semiconductor miniaturization technology provides strong hardware support for the development of artificial intelligence. For example, companies such as NVIDIA and AMD are using GPUs manufactured by 7 nm and below to provide higher parallel processing capability, especially suitable for complex AI computing tasks. Compared with the 14 nm process, the performance of GPU produced by the 7 nm process is improved by about 45%, while the power consumption is reduced by about 60%. Although the production cost is high (the cost of 7 nm process is about 35% higher than that of 14 nm process), the significant performance advantages of these GPUs make them more popular in the market in big data analysis and machine learning model training.
Challenges and Opportunities of Artificial Intelligence to Semiconductor Industry
The demand of artificial intelligence for semiconductor performance is growing
Artificial intelligence has an endless thirst for computing power. At present, advanced AI models such as GPT-4 require huge computing resources, which directly promotes the demand for high-performance semiconductors. For example, in order to run a large AI model, the required GPU computing power is at least 40TFLOPS (trillion floating-point operations/second), which has a great impact on power consumption, often between 250 and 300 watts. Considering the cost, the price of such high-performance GPU usually exceeds $1,000, which is not a small investment for some research organizations or small companies.
Adaptation and Innovation of AI Technology in Semiconductor Industry
In order to meet the needs of AI era, the semiconductor industry is actively innovating. AI-optimized chip design can now provide more refined computing efficiency and energy management, such as using heterogeneous computing architecture, which optimizes specific types of AI tasks through special hardware accelerators. AI chips with this design can improve the energy efficiency ratio of specific AI algorithms by more than 10 times, while the manufacturing cost is reduced due to large-scale production. However, the cost in the research and development stage is high, and the initial investment may reach tens of millions of dollars.
Artificial intelligence technology promotes the upgrading and transformation of semiconductor industry
Artificial intelligence technology has not only changed the design and manufacture of semiconductor products, but also promoted the upgrading and transformation of semiconductor industry. The application of intelligent manufacturing technologies such as predictive maintenance and automated quality control can reduce the rejection rate of semiconductor manufacturing, thus reducing the cost. For example, the manufacturing process optimized by AI algorithm can reduce the rejection rate from 3% to less than 1%, which means that in the process of producing 10,000 semiconductor wafers, the cost can be saved by millions of dollars. At the same time, such technological upgrading will also help shorten the time to market of products and enhance market competitiveness.
Interaction between Global Semiconductor Market and Artificial Intelligence
Present situation and future trend of global semiconductor market
The global semiconductor market has expanded rapidly in the past few years, and the market size has reached more than 500 billion US dollars in 2022. With the development of technologies such as 5G, Internet of Things (IoT) and autonomous driving, it is estimated that the market size will exceed 700 billion US dollars in 2025. Prices of semiconductors' core materials, such as silicon, have risen by more than 20% in the past year driven by market demand, resulting in higher manufacturing costs. In terms of power efficiency, modern semiconductor technologies such as FinFET and GAAFET have reduced the transistor size to less than 5 nanometers, which significantly improves the power efficiency and processing speed of the chip.
Semiconductor Industry Policies and Development Strategies of Artificial Intelligence in Various Countries
In order to ensure national security and economic development, governments of various countries have corresponding support policies for the semiconductor industry. For example, the U.S. government promised to invest 52 billion U.S. dollars to support the domestic semiconductor industry in the Chip Act introduced in 2021. The Chinese government also said it would invest more than 1 trillion yuan in the semiconductor industry in the next five years. These policies not only improve the R&D and manufacturing capabilities of domestic semiconductor industry, but also provide solid hardware support for the development of AI technology.
Competition and Cooperation between Semiconductor and AI in International Trade Environment
The globalization of semiconductor industry makes competition and cooperation coexist. As the two largest semiconductor markets and major countries in the development of artificial intelligence, the United States and China have fierce competition in high-end semiconductor equipment and materials. At the same time, in order to alleviate supply chain risks, these countries are also seeking diversified supply chain partners to ensure the stable supply of key semiconductor materials and equipment. For example, photoresist and noble gases in semiconductor production are key links in the global supply chain, and the price of photoresist can be as high as thousands of dollars at a time, while the price of noble gases has increased by more than 50% in the past year.
Heart of Intelligent Age: The Key Role of Semiconductor in the Development of Artificial Intelligence-China.exportsemi.com
