Artificial Intelligence Chip Market (Updated Version Available)
Artificial Intelligence Chip Market Size, Share & Trends Analysis, By Chipset Type {Graphic Processing Unit (GPU), Application Specific Integrated Circuits (ASIC), Field Programmable Gate Arrays (FPCA'S), Neural Network Processing (NNP), and Others}, Computing Type (Cloud and Edge Computing), Application (Machine Learning, Computer Vision, Robotic Process Automation, Context - Aware Computing, Predictive Analysis, and Natural Language Process), Function (Training and Interface), End User Industry (Media and Advertising, BFSI (Banking, Financial Services and Insurance), IT & Telecom, Retail, Healthcare, Automotive & Transportation, Agriculture & Defense) Region, Forecast Period 2022 - 2030
Report ID : RI_673745 | Date : February 2023 |
Format : 
Key Market Overview:
Artificial Intelligence Chip Market is estimated to reach over USD 289.59 Billion by 2030 from a value of USD 15.8 Billion in 2022, growing at a CAGR of 32.8% from 2022 to 2030.
A.I is a branch of computer science aiming to create smart machines capable of simulating human Intelligence or human thinking process. Hardware component assists A.I technology to acquire and process large volumes of raw data in order synthesize human like logic is known as artificial intelligence chipset. The (AI) chipsets are specialized computer hardware primarily designed to support and enhance the computational requirements of AI algorithms and applications.
A.I chipsets are designed to be highly efficient and are optimized for specific AI tasks, such as deep learning, computer vision, context – aware computing and natural language processing. Especially technologies like parallel processing and custom matrix-math instructions are applied with the help of A.I chipsets.
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Market Dynamics:
Driver:
Massive demand across industries in order to run deep learning algorithms:-
Wide variety of Industries across the globe including automotive, media, manufacturing and defense have invested considerable amounts in order to develop A.I solutions. For instance, automaker TESLA is developing self-driving cars based on deep learning, media houses are investing into A.I trained through deep learning for News Aggregation and Fraud News Detection. Furthermore, healthcare industry is broadly using A.I for Detecting Developmental Delay in Children. Aforementioned A.I models are generally trained with the help of Deep learning algorithms. Moreover, Deep learning algorithms relay upon artificial intelligence chips for their efficient and swift functioning. For instance, Microsoft invested USD 10 billion in a startup called open.ai to develop text-based A.I model capable of handling large scale enterprise functions.
Crucial applications in Natural Language Processing models:-
Current NLP modes extensively relay upon AI chipsets for their training and data processing post training. Google one of the leading tech companies developed an A.I chip called tensor processing unit (TPU). Moreover, TPU is extensively used in Natural language processing (NLP), TPU is capable in performing tasks such as language modeling, machine translation, and text classification. TPUs also support quantization, a technique that reduces the precision of the data in a model to reduce memory requirements and computation time, making them ideal for large-scale NLP models.
Restraint:
High power consumption is one of the major restraints hampering the growth of the AI chipset market. AI chipsets require high computational power to perform AI tasks, which results in high power consumption. This is a major restraint, especially for edge devices, where power consumption is limited due to the need for battery-powered operation. Moreover, high CAPEX requirement for A.I chip manufacturing also acts as an entry barrier for new players. Fabrication of A.I chips is a complex procedure and perquisites state of the art manufacturing facility with advance engineering capabilities. Human resource shortage is also a major restraint interrupting these industries potential growth. For instance, according to research by university of Massachusetts training AI models emit CO2 equivalent to 5 cars in their lifetime owing to massive power consumption.
Opportunities:
Applications in edge computing is one of the most lucrative opportunities in forth coming years for artificial intelligence chip market. Rising complexity and massive volumes of data have fueled the demand for edge computing in order to reduce latency and power consumption. Currently multiple industries such as automotive are using edge computing in self-driving vehicles as self-driving algorithms perquisites real time data processing for instant decision making. Moreover, A.I chipsets are very crucial for edge computing devices especially relay on A.I algorithms for real time data analysis and remote machine learning functions. The demand for AI chipset is set to rise with surging demand for edge computing devices.
Artificial Intelligence Chip Market Segmentation:
By Chipset Types
On the basis of chipset type the A.I processors are divided into Graphic processing unit (GPU), Application specific integrated Circuits (ASIC), Field programmable Gate Arrays (FPCA\'S), Neural network processing (NNP), and others. The GPU segment held the largest market in the year 2021 in similar year GPU segment accounted for 43 % of the total global sales in terms of revenue. GPU\'s are most preferred chipset types as GPU\'s have multiple cores allowing them to perform multiple complex computation task simultaneously also GPU\'s are excellent in computation of multiple parallel processes.
Moreover, the Field programmable Gate Arrays (FPCA\'S) are estimated to showcase considerable market growth over the forecasted period. According to estimates the defense and aerospace sectors will be the most dominating consumers for FPCA\'s in forth coming years.
By computing Type:-
By computing type, the AI chip market is bifurcated into cloud and edge computing. Edge computing type is currently dominating the market owing to its remote functioning and low latency aspects.
On the other hand, cloud computing also holds sizeable market share. Cloud computing is extremely efficient when it comes to process massive and complex data. In general, large enterprises leverage cloud computing for their AI algorithms functioning as interface.
By Application
The application segment is divided into Machine learning, Computer vision, Robotic process automation, Context – aware computing, Predictive analysis, Natural language process. Machine learning applications has the dominant market share. Presently most of the key payers across various industries are using machine learning to train their algorithms that are capable of performing multiple business functions including auto pricing, product marking optimization, predictive inventory is few among others.
The natural language processing (NLP) application is also estimated to showcase substantial growth CAGR in the near future. The primary reason for the exponential growth is due to functioning of NPL including assisting in functions such as Sentiment Analysis, Text Classification, Chabot\'s & Virtual Assistants, Text Extraction, Machine Translation, Text Summarization and Market Intelligence. For instance, according to Statista the NLP market is estimated to reach USD 45 billion in the year 2025.
By function:-
The function segment is bifurcated into training and interface. The training segment has majority market share among the two. The primary reason for the domination of training segment in A.I functionality is due to requirement of high computing power during the model training phase. As A.I chips are specially designed to handle high computing process for A.I training models due to their extensive preference in comparison to other options.
Interface segment is estimated to showcase moderate growth with AI\'s adoption in large scale enterprises is considered to be its main driving factor.
By end-user industry:-
By end-user industry this segment is divided into media and advertising, BFSI (banking, financial services and insurance), IT and telecom, retail, healthcare, automotive and transportation, agriculture, defense. The retail segment held dominating market share in the year 2021. Most of the A.I chips manufactured are used for Edge computing applications in retail electronic devices.
On the other hand, the healthcare segment is estimated to showcase fastest CAGR in this market. Most of the top medical and healthcare institutions are integrating A.I with medical equipment\'s for functions like Prediction and identification of diseases, data classification and analysis for disease outbreak, optimization of medical therapy and diagnostic support. For instance, according to the U.S. Food and Drug Administration (FDA) as of January 2023.United State have 520 marker-cleared artificial intelligence (AI) medical algorithms available.
By Region
The regional segment comprises Europe, Asia Pacific, North America, Middle East and Africa and Latin America. North America is estimated to be the most dominant region for A.I chipset market in the year 2021. Primary reason supporting this domination is presence of large number of market leaders in US. Most of the developers and end users of A.I chipsets are present in U.S. ultimately fueling the demand for AI chips in this region.
In forth coming yeas the Asia- Pacific region is estimated to showcase exponential market growth. Continuous technological advancements in countries such as
South Korea and Japan are estimated to propel the demand for A.I chipsets in this region.
Artificial Intelligence Chip Market Competitive Landscape:
The report on Artificial Intelligence Chip Market presents a comprehensive analysis of macroeconomic factors such as market dynamics, SWOT analysis, segmental analysis and others. Moreover, in terms of market players, the data is related to business collaborations, mergers and acquisitions, product innovations, and strategies adopted by competitors. These factors are studied to offer an insightful understanding of current market circumstances. Following are the major market players that comprise the latest market concentration–
• NVIDIA
• Microsemi Corporation
• Mythic, Inc.
• Nec Corporation
• Korea Electronic Certification Authority, Inc. (Ai Brain, Inc.)
• Nvidia Corporation
• Nxp Semiconductors N.V.
• Qualcomm Incorporated
• Samsung Electronics Co. Ltd.
• Shanghai Think-Force Electronic Technology Co. Ltd.
• Sk Hynix, Inc.
• Softbank Group Corp. (Arm Holdings Plc)
Taiwan Semiconductor Manufacturing Company Limited
• Tenstorrent Inc.
• Texas Instruments Incorporate
Recent Developments:
• Intel acquired A.I chip maker named Habana technologies the deal was valued at 2 Billion USD.
Artificial Intelligence Chip Market is estimated to reach over USD 289.59 Billion by 2030 from a value of USD 15.8 Billion in 2022, growing at a CAGR of 32.8% from 2022 to 2030.
A.I is a branch of computer science aiming to create smart machines capable of simulating human Intelligence or human thinking process. Hardware component assists A.I technology to acquire and process large volumes of raw data in order synthesize human like logic is known as artificial intelligence chipset. The (AI) chipsets are specialized computer hardware primarily designed to support and enhance the computational requirements of AI algorithms and applications.
A.I chipsets are designed to be highly efficient and are optimized for specific AI tasks, such as deep learning, computer vision, context – aware computing and natural language processing. Especially technologies like parallel processing and custom matrix-math instructions are applied with the help of A.I chipsets.
| Report Attributes | Report Details |
| Study Timeline | 2016-2030 |
| Market Size in 2030 (USD Billion) | USD 289.59 Billion |
| CAGR (2022-2030) | 32.8 % |
| Base Year | 2021 |
| By Chipset Type | Graphic processing unit (GPU), Application specific integrated Circuits (ASIC), Field programmable Gate Arrays (FPCA\'S), Neural network processing (NNP), and others. |
| By Computing Type | Cloud and edge computing. |
| By Application | Machine learning, Computer vision, Robotic process automation, Context – aware computing, Predictive analysis, and Natural language process |
| By function | Training and Interface |
| By End User industry | Media and Advertising, BFSI (Banking, Financial Services and Insurance), IT and Telecom, Retail, Healthcare, Automotive and Transportation, Agriculture and Defense |
| By Geography | |
| Key Players | NVidia, Microsemi Corporation, Mythic, Inc., Nec Corporation, Korea Electronic Certification Authority, Inc. (Ai Brain, Inc.), Nvidia Corporation, Nxp Semiconductors N.V., Qualcomm Incorporated, Samsung Electronics Co. Ltd., Shanghai Think-Force Electronic Technology Co. Ltd., Sk Hynix, Inc., Softbank Group Corp. (Arm Holdings Plc), Taiwan Semiconductor Manufacturing Company Limited, Tens torrent Inc., Texas Instruments Incorporate. |
Market Dynamics:
Driver:
Massive demand across industries in order to run deep learning algorithms:-
Wide variety of Industries across the globe including automotive, media, manufacturing and defense have invested considerable amounts in order to develop A.I solutions. For instance, automaker TESLA is developing self-driving cars based on deep learning, media houses are investing into A.I trained through deep learning for News Aggregation and Fraud News Detection. Furthermore, healthcare industry is broadly using A.I for Detecting Developmental Delay in Children. Aforementioned A.I models are generally trained with the help of Deep learning algorithms. Moreover, Deep learning algorithms relay upon artificial intelligence chips for their efficient and swift functioning. For instance, Microsoft invested USD 10 billion in a startup called open.ai to develop text-based A.I model capable of handling large scale enterprise functions.
Crucial applications in Natural Language Processing models:-
Current NLP modes extensively relay upon AI chipsets for their training and data processing post training. Google one of the leading tech companies developed an A.I chip called tensor processing unit (TPU). Moreover, TPU is extensively used in Natural language processing (NLP), TPU is capable in performing tasks such as language modeling, machine translation, and text classification. TPUs also support quantization, a technique that reduces the precision of the data in a model to reduce memory requirements and computation time, making them ideal for large-scale NLP models.
Restraint:
High power consumption is one of the major restraints hampering the growth of the AI chipset market. AI chipsets require high computational power to perform AI tasks, which results in high power consumption. This is a major restraint, especially for edge devices, where power consumption is limited due to the need for battery-powered operation. Moreover, high CAPEX requirement for A.I chip manufacturing also acts as an entry barrier for new players. Fabrication of A.I chips is a complex procedure and perquisites state of the art manufacturing facility with advance engineering capabilities. Human resource shortage is also a major restraint interrupting these industries potential growth. For instance, according to research by university of Massachusetts training AI models emit CO2 equivalent to 5 cars in their lifetime owing to massive power consumption.
Opportunities:
Applications in edge computing is one of the most lucrative opportunities in forth coming years for artificial intelligence chip market. Rising complexity and massive volumes of data have fueled the demand for edge computing in order to reduce latency and power consumption. Currently multiple industries such as automotive are using edge computing in self-driving vehicles as self-driving algorithms perquisites real time data processing for instant decision making. Moreover, A.I chipsets are very crucial for edge computing devices especially relay on A.I algorithms for real time data analysis and remote machine learning functions. The demand for AI chipset is set to rise with surging demand for edge computing devices.
Artificial Intelligence Chip Market Segmentation:
By Chipset Types
On the basis of chipset type the A.I processors are divided into Graphic processing unit (GPU), Application specific integrated Circuits (ASIC), Field programmable Gate Arrays (FPCA\'S), Neural network processing (NNP), and others. The GPU segment held the largest market in the year 2021 in similar year GPU segment accounted for 43 % of the total global sales in terms of revenue. GPU\'s are most preferred chipset types as GPU\'s have multiple cores allowing them to perform multiple complex computation task simultaneously also GPU\'s are excellent in computation of multiple parallel processes.
Moreover, the Field programmable Gate Arrays (FPCA\'S) are estimated to showcase considerable market growth over the forecasted period. According to estimates the defense and aerospace sectors will be the most dominating consumers for FPCA\'s in forth coming years.
By computing Type:-
By computing type, the AI chip market is bifurcated into cloud and edge computing. Edge computing type is currently dominating the market owing to its remote functioning and low latency aspects.
On the other hand, cloud computing also holds sizeable market share. Cloud computing is extremely efficient when it comes to process massive and complex data. In general, large enterprises leverage cloud computing for their AI algorithms functioning as interface.
By Application
The application segment is divided into Machine learning, Computer vision, Robotic process automation, Context – aware computing, Predictive analysis, Natural language process. Machine learning applications has the dominant market share. Presently most of the key payers across various industries are using machine learning to train their algorithms that are capable of performing multiple business functions including auto pricing, product marking optimization, predictive inventory is few among others.
The natural language processing (NLP) application is also estimated to showcase substantial growth CAGR in the near future. The primary reason for the exponential growth is due to functioning of NPL including assisting in functions such as Sentiment Analysis, Text Classification, Chabot\'s & Virtual Assistants, Text Extraction, Machine Translation, Text Summarization and Market Intelligence. For instance, according to Statista the NLP market is estimated to reach USD 45 billion in the year 2025.
By function:-
The function segment is bifurcated into training and interface. The training segment has majority market share among the two. The primary reason for the domination of training segment in A.I functionality is due to requirement of high computing power during the model training phase. As A.I chips are specially designed to handle high computing process for A.I training models due to their extensive preference in comparison to other options.
Interface segment is estimated to showcase moderate growth with AI\'s adoption in large scale enterprises is considered to be its main driving factor.
By end-user industry:-
By end-user industry this segment is divided into media and advertising, BFSI (banking, financial services and insurance), IT and telecom, retail, healthcare, automotive and transportation, agriculture, defense. The retail segment held dominating market share in the year 2021. Most of the A.I chips manufactured are used for Edge computing applications in retail electronic devices.
On the other hand, the healthcare segment is estimated to showcase fastest CAGR in this market. Most of the top medical and healthcare institutions are integrating A.I with medical equipment\'s for functions like Prediction and identification of diseases, data classification and analysis for disease outbreak, optimization of medical therapy and diagnostic support. For instance, according to the U.S. Food and Drug Administration (FDA) as of January 2023.United State have 520 marker-cleared artificial intelligence (AI) medical algorithms available.
By Region
The regional segment comprises Europe, Asia Pacific, North America, Middle East and Africa and Latin America. North America is estimated to be the most dominant region for A.I chipset market in the year 2021. Primary reason supporting this domination is presence of large number of market leaders in US. Most of the developers and end users of A.I chipsets are present in U.S. ultimately fueling the demand for AI chips in this region.
In forth coming yeas the Asia- Pacific region is estimated to showcase exponential market growth. Continuous technological advancements in countries such as
South Korea and Japan are estimated to propel the demand for A.I chipsets in this region.
Artificial Intelligence Chip Market Competitive Landscape:
The report on Artificial Intelligence Chip Market presents a comprehensive analysis of macroeconomic factors such as market dynamics, SWOT analysis, segmental analysis and others. Moreover, in terms of market players, the data is related to business collaborations, mergers and acquisitions, product innovations, and strategies adopted by competitors. These factors are studied to offer an insightful understanding of current market circumstances. Following are the major market players that comprise the latest market concentration–
• NVIDIA
• Microsemi Corporation
• Mythic, Inc.
• Nec Corporation
• Korea Electronic Certification Authority, Inc. (Ai Brain, Inc.)
• Nvidia Corporation
• Nxp Semiconductors N.V.
• Qualcomm Incorporated
• Samsung Electronics Co. Ltd.
• Shanghai Think-Force Electronic Technology Co. Ltd.
• Sk Hynix, Inc.
• Softbank Group Corp. (Arm Holdings Plc)
Taiwan Semiconductor Manufacturing Company Limited
• Tenstorrent Inc.
• Texas Instruments Incorporate
Recent Developments:
• Intel acquired A.I chip maker named Habana technologies the deal was valued at 2 Billion USD.
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