The Artificial Intelligence Chipsets Market size is estimated to grow from USD 7.2 Billion in 2020 to USD 80.6 Billion by 2027, growing at a CAGR of 41.2% during the forecast year from 2021 to 2027.
The base year for calculation in this Artificial Intelligence Chipsets Market business report is taken as 2020 while the historic year is 2019 which will tell how the market will perform in the forecast years by informing what the market definition, classifications, applications, & engagements are. This Artificial Intelligence Chipsets Market report is also all-embracing of the data which covers market definition, classifications, applications, engagements, market drivers & market restraints that are based on the SWOT analysis. A large scale Artificial Intelligence Chipsets Market report is a comprehensive study about the market which tells about the market status in the forecast period of 2021-2027.
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The artificial intelligence chipsets market size is estimated to grow from USD 7.2 Billion in 2020 to USD 80.6 Billion by 2027, growing at a CAGR of 41.2% during the forecast year from 2021 to 2027. Major drivers for the market are increasingly large and complex datasets driving the need for AI, the adoption of AI for improving consumer services and reducing operational costs, the growing number of AI applications, the improving computing power, and growing adoption of deep learning and neural networks.
COVID-19 Impact on the Global AI (chipsets) Market
The market is likely to witness a slight plunge in terms of year-on-year growth in 2020. This is largely attributed to the affected supply chains and limited adoption of AI in various end-user industries in 2020 due to the lockdowns and shifting priorities of different industries. The ongoing COVID-19 pandemic has caused disruptions in economies. It is likely to cause supply chain mayhem and eventually force companies and entire industries to rethink and adapt to the global supply chain model. Many manufacturing companies have halted their production, which has collaterally damaged the supply chain and the industry. This disruption has caused a delay in the adoption of AI-based software and hardware products. The industries have started to restructure their business model for 2020, and many SMEs and large manufacturing plants have halted/postponed any new technology upgrade in their factories to recover from the losses caused by the lockdown and economic slowdown. COVID-19 has impacted the educational industries rather positively, with ed-tech companies adopting AI technology to impart education during the lockdown. Ed-tech firms have deployed AI tools to enhance online learning and virtual classroom experience for students.
Several industries are worse hit by this pandemic, but some industries are profiting from this pandemic. However, the adoption of AI is expected to grow. Therefore, we can say the COVID-19 will drive the AI (chipsets) market for certain industries.
AI (chipsets) Market Dynamics
Driver: Growing adoption of deep learning and neural networks
Deep learning is a subset of machine learning and AI that has networks capable of unsupervised learning from unstructured data. Although deep learning is known for a while, it started trending in 2016 when Google’s AI robot player defeated grandmaster Lee Sedol in the game of AlphaGo. Since then, deep learning has been considered as a formidable tool for enterprises that require actionable insights and enable automated responses to large unstructured data. Many of the advanced automation found in enterprise AI platforms is attributed to the growth and adoption of machine learning and deep learning. Neural networks are algorithms that recognize the underlying relationships in data sets through a process that mimics a human brain. Artificial Neural Networks (ANN) are replacing traditional ML models to advance precise modeling. At the same time, Convolutional Neural Networks (CNN) translates the power of deep learning to computer vision. Deep learning models ANN are seeing significant adoption in image processing in medicinal services, defense, transportation, and others.
Restraint: Lack of skilled AI workforce
AI is a complex system, and for developing, managing, and successfully implementing AI systems, companies require a workforce with certain skill sets. For instance, people dealing with AI systems should know about technologies, such as cognitive computing, ML & machine intelligence, deep learning, and image recognition. Also, the integration of AI solutions in the existing systems is a difficult task that requires extensive data processing to replicate the behavior of a human brain. Even a minor error can fail the system or can adversely affect the desired result. Furthermore, the absence of professional standards and certifications in AI/ML technologies is curbing the growth of AI. Additionally, the AI service providers are facing challenges to deploy/service their solutions at their customer sites. This is because of a lack of technology awareness and limitations of AI experts.
Opportunities: Increasing focus on human-aware AI systems
The expectation aimed at, during the emergence of AI technologies, was to make them human-aware, i.e., developing models with the characteristics of human thinking. However, creating interactive and scalable machines remains a challenge among the developers of AI machines. Additionally, increasing human interference with AI techniques has introduced new research challenges, i.e., interpretation and presentation challenges, such as interaction issues with automating parts and intelligent control of crowdsourcing parts. Interpretation challenges include the challenges face by AI machines in understanding human input, such as knowledge and specific directives, among others. Presentation challenges include issues related to delivering the AI system’s output and feedback. Thus, the development of human-aware AI systems remains the foremost opportunity among AI developers.
Challenges: Limited structured data
Data is one vital source to train and develop a complete and robust AI system. Earlier datasets were mostly structured and were also mostly entered manually. However, the growing digitization globally and technology trends like the Internet of Things (IoT) and Industry 4.0 has resulted in data from wearable devices, smart thermostats, connected cars, IP cameras, smart appliances, manufacturing machines, industrial equipment, and various other remotely connected devices. This data is mainly unstructured and is in the form of text, voice, and images, among others. Lack of orderly internal structure limits the developers to extract value. However, training a machine learning tools developer requires high-quality labeled data, along with skilled human trainers. Extracting and labeling unstructured data required lots of skilled workforce and time. Thus, to develop an efficient AI system, structured data plays a major role. On the other hand, the company is now practicing developing insights from semi-structured data (it is a combination of structured and unstructured data), which enables information from grouping and hierarchies. However, analytics tools and solutions for semi-structured data are at the nascent stage.
Memory segment is expected to grow the fastest from 2020 to 2026
AI technologies, such as machine learning, computer vision, and predictive analytics, require a large volume of data to train, test, and validate neural network algorithms, which may present storage challenges for data administration. Memory requirement has increased significantly in recent years. High-bandwidth memory is being developed and deployed for AI applications, independent of its computing architecture. A few start-up companies are exploring high-bandwidth parallel file systems to increase both throughput and efficiency.
The cybersecurity industry held the largest size of the AI (chipsets) market in 2019
AI is significantly used in antivirus and antimalware solutions owing to the rise in cybersecurity attacks across the world. Increasing use of mobile devices for a wide range of applications, such as social networking, e-mails, remote monitoring, phone banking, and data storage, opens doors for hackers to attack, thereby making networks more vulnerable to risks. The rapid adoption of cloud-based services, along with the user-friendly approach of antivirus/antimalware solutions, is contributing to the growth of this end-user industry of the AI (chipsets) market.
Deep learning technology expected to be adopted extensively during the forecast period
Deep learning is a class of ML-based on multiple algorithms for creating relationships among data. Deep learning uses artificial neural networks to learn multiple levels of data, such as texts, images, and sounds. Its algorithms help in identifying patterns from a set of unstructured data. The growing application of deep learning algorithms is a major driving force for the AI (chipsets) market. Presently, deep learning technology is used in voice recognition, fraud detection, voice search, recommendation engines, sentiment analysis, image recognition, and motion detection, among others.
The adoption of AI (chipsets) was significant in the North American region
The growing concern about the security of critical infrastructure and sensitive data has increased government intervention in recent years and resulted in the adoption of AI (chipsets) in security applications. High consumerization of personal care products—routine checkup medical tools and wearable devices, is increasing the growth of AI-enabled healthcare devices in North America, thereby driving the growth of the AI (chipsets) market. Government support, especially in the US, is driving the growth of AI chipsets in the automotive application in the region.
Market Key Players
AI (chipsets)market is dominated by globally established players such as NVIDIA (US), Intel (US), Samsung Electronics (South Korea), Xilinx (US), Micron (US)
AI (chipsets) market segmentation:
In this report, the AI (chipsets) market has been segmented into the following categories:
Based on Hardware:
Based on Technology:
- Machine Learning
- Natural Language Processing
- Context-aware Computing
- Computer Vision
- Predictive Analysis
Based on Function:
Based on the End-user Industry:
- Human Resources
Based on Region:
- North America
- Asia Pacific
- Rest of the World (RoW)
- In May 2020, NVIDIA launched two powerful products for its EGX Edge AI platform — the EGX A100 for larger commercial off-the-shelf servers and the tiny EGX Jetson Xavier NX for micro-edge servers — delivering high-performance, secure AI processing at the edge. With the NVIDIA EGX Edge AI platform, hospitals, stores, farms, and factories can carry out real-time processing and protection of massive amounts of data streaming from trillions of edge sensors. The platform makes it possible to securely deploy, manage, and update fleets of servers remotely.
- In May 2020, NVIDIA announced that the first GPU based on the NVIDIA Ampere architecture, the NVIDIA A100, is in full production and shipping to customers worldwide. The A100 draws on design breakthroughs in the NVIDIA Ampere architecture — offering the company’s largest leap in performance to date within its eight generations of GPUs — to unify AI training and inference and boost performance by up to 20x over its predecessors. A universal workload accelerator, the A100 is also built for data analytics, scientific computing, and cloud graphics.
- In February 2020, Micron, together with technology company Continental (Germany), announced plans to enter into a collaboration agreement to explore and adapt Micron’s deep learning accelerator for next-generation machine learning automotive applications. This agreement brings together two world leaders in the automotive and memory markets to advance machine learning to meet the extreme memory requirements of modern vehicles.
All viewpoints in the report are based on iterative validation by engaging influencer, experts of the market, whose opinions supersede all other research methodologies. Both primary and secondary approach are used and detailed product portfolio / service offering were analysed and have been presented in a separate chapter of competitive landscape along with company profile.
Detailed Insights on Market Concentration Rate:
CR4, CR8 and HHI Index Analysis of Overall Market
Comparative Market Share Analysis (Y-o-Y)
Emerging Players – Heat Map Analysis
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Table of Content:
Chapter 1: Introduction and Scope
Chapter 2: Key Company Profiles
Chapter 3: Artificial Intelligence Chipsets Market Explanations, Share and Forecast across type, application and geography
Chapter 4: Artificial Intelligence Chipsets Industry Consumption by Regions
Chapter 5: Market Explanation of Asia Pacific region
Chapter 6: Market Explanations of Europe region
Chapter 7: Market Explanation of Asia Pacific region
Chapter 8: Market Explanations of North America region
Chapter 9: Market Explanations of Middle East and Africa region
Chapter 10: Key Important features of the Artificial Intelligence Chipsets market
Chapter 11: Key trends of the market and the Artificial Intelligence Chipsets market Opportunities
Chapter 12: Industrial Chain, Sourcing Strategy and Downstream Buyers
Chapter 13: Research Findings and Conclusion
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