With the growing demand for real-time deep learning, specialized edge artificial intelligence (AI) hardware that allows rapid deep learning on the device has become essential. The current standard (cloud-based) artificial intelligence (AI) solution is inadequate for covering bandwidth, providing low latency and ensuring data privacy. Therefore, artificial intelligence (AI) tasks must be relocated to the Edge. Edge artificial intelligence (AI) may operate on various hardware platforms, ranging from powerful neural processing processors to standard MCUs. Edge artificial intelligence (AI) hardware devices include IoT devices and machines, increasing their demand in the global market.
According to Data Bridge Market Research the Edge Artificial Intelligence (AI) Hardware Market accounted for USD 744.03 million in 2021, and expected to reach USD 4,030.32 million by 2029. The market is expected to grow with a CAGR of 20.65% in the forecast period of 2022 to 2029.
“Growing usage of on-device AI processor for image analysis is expected to drive the market growth”
Artificial intelligence (AI) mobile processor power computational imaging applications in drones, self-driving cars and wearable electronics, robots. Artificial intelligence (AI) based vision processing units (VPU) can help drones reduce the risk of accidents and make better decisions, which will contribute to the increasing demand for personal and industrial drones. Visual awareness and computational imaging applications increase mobile devices by changing complex optics with simpler lens assemblies, combining images captured by heterogeneous sensors such as depth sensors RGB and infrared (IR) and extracting contextual metadata from video streams and still images. These factors allow the usage of vision processing units (VPU) in mobile handsets, wearable devices, tablets, and personal robots. Therefore, growing usage of on-device AI processors for image analysis is expected to drive the market growth.
What restraints the growth of edge artificial intelligence (AI) hardware market?
“Limitations associated with edge artificial intelligence (AI) devices”
Pre-trained machine learning models are employed for edge artificial intelligence (AI) inference. These models automatically adjust user data and requirements. Training a model needs a significant amount of computer power, but edge artificial intelligence (AI) devices have limited access to training data which is likely to hamper the market growth. Moreover, edge artificial intelligence (AI) can perform only small transfer learning tasks but cannot perform deep learning tasks which also hampers the market growth.
Segmentation: Edge Artificial Intelligence (AI) Hardware Market
The edge artificial intelligence (AI) hardware market is segmented on the basis of device, processors, power consumption, process and end user industry.
- On the basis of device, the edge artificial intelligence (AI) hardware market is segmented into smartphones, cameras, robots, wearable, smart speaker, automotive, smart mirror.
- Based on processors, the edge artificial intelligence (AI) hardware market is segmented into central processing unit (CPU), graphics processing unit (GPU), application-specific integrated circuit (ASIC) and others.
- On the basis of power consumption, the edge artificial intelligence (AI) hardware market is segmented into less than 1w, 1-3w, 3-5w, 5-10w, more than 10w.
- On the basis of process, the edge artificial intelligence (AI) hardware market is segmented into training, inference.
- Based on end user industry, the edge artificial intelligence (AI) hardware market is segmented into consumer electronics, smart home, automotive and transportation, government, healthcare, industrial, aerospace and defence, construction, others.
Regional Insights: North America is expected to dominate the edge artificial intelligence (AI) hardware market
North America is expected to dominate the market and grow significantly because of the high adoption rate of artificial intelligence (AI) based servers within the region. Moreover, large presence of prominent artificial intelligence (AI) technology providers will further boost the growth of the edge artificial intelligence (AI) hardware market in this region.
In addition, the Asia-Pacific region is likely to register highest growth rate during the forecast period of 2022 to 2029 due to the creation of "new infrastructure" projects, for instance 5G networks and data centers in this region. Furthermore, increasing awareness regarding artificial intelligence (AI) will positively influence the market growth the market growth.
Recent Developments: Edge Artificial Intelligence (AI) Hardware Market
- In 2021, Intel Corporation revealed its 3rd generation Intel Xeon Scalable processor. The main aim of this processor to provide a balanced architecture with advanced security capabilities built-in artificial intelligence, and crypto acceleration.
- In 2020, Hyundai Motor which is a South Korean multinational automotive manufacturer collaborated with NVIDIA to use NVIDIA DRIVE infotainment and artificial intelligence (AI) platforms for its upcoming Kia, Hyundai, and Genesis models. NVIDIA is an American multinational technology company based in Santa Clara,
To know more about the study visit, https://www.databridgemarketresearch.com/jp/reports/global-edge-ai-hardware-market
The Prominent Key Players Operating in the Edge Artificial Intelligence (AI) Hardware Market Include:
- Cisco Systems, Inc. (U.S.)
- IBM (U.S.)
- Intel Corporation (U.S.)
- SAMSUNG (South Korea)
- Google (U.S.)
- Microsoft (U.S.)
- Micron Technology, Inc. (U.S.)
- NVIDIA Corporation (U.S.)
- Oracle (U.S.)
- Arm Limited (U.K.)
- Advanced Micro Devices, Inc (U.S.)
- Dell Inc. (U.S.)
- Hewlett Packard Enterprises Development LP (U.S.)
- Habana Labs Ltd. (US)
- Synopsys, Inc (U.S.)
- Nutanix (U.S.)
- Amazon Web Services, Inc (U.S.)
Above are the key players covered in the report, to know about more and exhaustive list edge artificial intelligence (AI) hardware market companies contact, https://www.databridgemarketresearch.com/jp/contact
Research Methodology: Global Edge Artificial Intelligence (AI) Hardware Market
Data collection and base year analysis are done using data collection modules with large sample sizes. The market data is analyzed and estimated using market statistical and coherent models. In addition, market share analysis and key trend analysis are the major success factors in the market report. The key research methodology used by DBMR research team is data triangulation which involves data mining, analysis of the impact of data variables on the market, and primary (industry expert) validation. Apart from this, data models include Vendor Positioning Grid, Market Time Line Analysis, Market Overview and Guide, Company Positioning Grid, Company Market Share Analysis, Standards of Measurement, global vs Regional and Vendor Share Analysis. Please request analyst call in case of further inquiry.
