Global Artificial Intelligence (AI) in Hardware Market Ecosystem by Components; by Product; by Technology; by End Users (Automotive, Retail, Consumer Electronics, IT & Telecom, Others); by Region and Forecast - 2027

Artificial Intelligence (AI) in Hardware Market Size

The market revenue of artificial intelligence in hardware market ecosystem was estimated at US$ 21.44 Bn in 2019 and the number is expected to US$ 65.44 Bn, registering a growth of 25.8% during the review period from 2020 to 2027.

The emergence of high-end personal computing devices, smartphones and gaming devices in the past decade, semiconductor companies found themselves in a difficult spot to cope up with the architecture and software layers of technology, that led to several developments. The penetration of artificial intelligence has allowed semiconductor organizations to acquire close to 45% of the entire value from the technology end. With the penetration of artificial intelligence, demand for semiconductors intensified fivefold, much more than when semiconductors were used for non-AI applications. Most of the opportunities for AI hardware emerged from data centers and the edge.

Computing growth will emerge from greater demand for AI-enabled applications at data centers. GPU’s are heavily used for most of the training applications, but they are soon expected to lose market share of ASICs, to the point that both components will hold about equal market share by 2027. Further, in addition to ASICs and GPUs, FPGAs too will have a prominent role to play in the AI hardware ecosystem in the coming years, mostly for customized data center applications. Moreover, CPUs are expected to lose market share to ASICs as well, due to the growing application of deep learning applications. Moreover, in the case of edge applications, most of the training is done through laptops and other forms of personal computing devices, and these devices are expected to enhance their role by recording their data and playing a vital role in on-site training. For instance, drills that are used during oil and gas exploration help to generate data that is related to the well’s geological characteristics can be used in training models. At present for edge applications, the training market is evenly distributed between ASICs and CPUs. However, this scenario is expected to change in the coming years as ASICs will clearly dominate the AI hardware ecosystem for edge applications. In addition to this, FPGAs will rise to prominence as well in the coming years.

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The memory segment accounted for a significant share in the global artificial intelligence hardware market ecosystem in 2019 and is expected to display similar trends in the coming years as well. Artificial intelligence-enabled applications have high memory requirements as computing layers within deep neural networks pass input data to thousand of cores very quickly. DRAM is commonly used to store input data, weigh model parameters, and perform other imperative functions. Due to these reasons the memory segment is expected to display significant prominence in the coming years. In addition, the storage segment is another area that is expected to grow significantly in the coming years. AI-enabled applications generate huge volumes of data, which roughly translates to 93 exabytes per year. Further, developers a using more data in the field of deep learning, which is calling for more storage space.

Based on deployment, cloud solutions accounted for majority of the market share in the AI hardware ecosystem in 2019 and is expected to display similar trends in the coming years. Cloud is a perfect location for training purposes because it provides access to huge amounts of data from several servers, and greater the information on AI application reviews during training, the better will be the development of the algorithm, In addition, cloud can help to reduce costs since it allows GPUs and other components to train multiple AI models.

The report incorporates the study of the artificial intelligence hardware ecosystem that focuses on the product types, application, devices, material, technology, deployment, end-user operated in the business. Major players operating in the space of artificial intelligence hardware ecosystem include NVIDIA Corporation, Intel Corporation, AMD Inc, Qualcomm Inc, Xilinx Inc, NXP Semiconductors, Huawei Technologies, Apple Inc, Samsung Electronics, and various others

Report Scope:

A study is an effective tool for addressing Research insights relevant for business strategies like:

• New product launch
• New client acquisition
• New opportunity mapping (market level and geography level)
• Competitive benchmarking
• Cost optimization strategies
• Inorganic expansion plans

The market segments are identified and analyzed keeping in mind the artificial intelligence in hardware market ecosystem are given below:

• Product Type
  • CPU
  • GPU
  • ASIC
  • FPGA
  • Memory
  • Storage
  • Modules
• Application
  • Training & Simulation
  • Driver Monitoring Systems
  • Surveillance & Security
  • Imaging & Diagnosis
  • Robotic Surgery
  • Disaster Management
  • Visual Inspection
  • Others
• Technology
  • Machine Learning
    • Supervised Learning
    • Un-supervised Learning
    • Deep Learning
    • Others
  • Computer Vision
  • Others
• Material
  • Silicon
  • GaN
  • Glass
  • Metal
  • Others
• Devices
  • Smartphones & Tablets
  • Personal Computing Devices
  • Autonomous Robots
  • UAVs/UGVs
  • HUD
  • Others
• Deployment
  • Cloud
    • Cloud Platforms
      • Private Cloud
      • Public Cloud
      • Hybrid Cloud
      • Community Cloud
    • Cloud Services
      • SaaS
      • IaaS
      • PaaS
      • Others
  • On-premise
• End-use Industry
  • Healthcare
  • Automotive
  • Aerospace & Defense
  • Oil & Gas
  • Transportation & Logistics
  • Public & Social Sector
  • BFSI
  • Retail
  • Telecommunications
  • Others

Regional Overview of Global Artificial Intelligence Hardware Market Ecosystem

Geographically, North America and Europe dominated the global artificial intelligence in hardware market ecosystem, both accounting for a share of more than 70% of the global artificial intelligence hardware ecosystem in 2019. However, Asia Pacific is expected to be in the limelight in the coming years with countries such as China, India, South Korea, and Japan taking the center stage in terms of demand by the end of the forecast period. Organizations across South Korea that includes several startups, all have AI as their offering. For instance, North Korea’s indigenous smartphone brand called ‘Jindallae 6’ and ‘Jindallae 7’ have included artificial intelligence and facial recognition technology in the phones.

The major players operating in the global Artificial Intelligence Hardware Ecosystem are as follows:

Company	Ecosystem Positioning	Total Revenue (2019)	Industry	Region
Nvidia Corporation	Hardware Manufacturers	$ 11.7 Billion	Semiconductors	Global
Intel Corporation	Hardware Manufacturers	$70.8 Billion	Semiconductors	Global
Qualcomm Inc.	Hardware Manufacturers	$22.7 Billion	Semiconductors	Global
Samsung Electronics Co., Ltd	Hardware Manufacturers	$210 Billion	Consumer Electronics and Semiconductors	Global
Xilinx, Inc.	Hardware Manufacturers	$2.5 Billion	Semiconductors	Global

Very few markets have the interconnectivity with other markets like AI. Our Interconnectivity module focuses on the key nodes of heterogenous markets in detail. GPU, ASIC, FPGA, Machine Learning, Deep Learning, Computer Vision are some of our key researched markets. 

COVID-19 Impact on the Global Artificial Intelligence Hardware Market Ecosystem

The COVID-19 pandemic has swept the globe, with strict lockdowns imposed across the country. The economic growth of major countries plummeted, purchasing power declined significantly, production facilities were shut down, and businesses struggled to survive. Further, owing to the pandemic cross border trading activities, semiconductor shipments declined, which led to a slowdown in the artificial intelligence hardware market. However, as most countries lifted their respective lockdowns, production facilities restarted, and cross border trading activities resumed slowly, resulting in the stabilization of semiconductor shipments.

Glance on Global Artificial Intelligence Hardware Market Ecosystem Trends:

• Processors are developed natively with more security and encryption, especially for ADAS applications
• Increasing use of voice-based commands in the Next Generation Smartphone industry will fuel the growth of NLP
• GPU has emerged strongly in the autonomous vehicle application. GPU's help in processing most of the complex vehicle data in real-time more efficiently
• GPU's have the capacity to process complex data. Active safety systems and ADAS in automobiles utilize GPU's for processing
• In the automotive industry, there exists a competition between GPU and FPGA. Whereas GPU's have a high demand in ADAS and autonomous vehicle application compared with the FPGA's

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