Artificial Intelligence for Edge Devices Market Size (USD 19.11 billion by 2032) with 20.3% CAGR
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Published Via 11Press: The Artificial Intelligence for Edge Devices market involves the application of AI algorithms and tools on edge devices such as smartphones, IoT devices, and other connected gadgets, allowing them to perform intelligent tasks without relying on a central cloud or data center.
The market is expected to grow significantly due to increased adoption of edge computing and proliferation of IoT devices. Edge AI is becoming more and more popular due to its real-time decision-making capabilities while reducing latency and bandwidth requirements.
The global artificial intelligence for edge devices market size is expected to be worth around USD 19.11 Bn by 2032 from USD 3.01 Bn in 2022, growing at a CAGR of 20.3% during the forecast period from 2022 to 2032.
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Key Takeaways
- Growing Demand for Edge Computing: The explosion of IoT devices and the need for real-time processing have propelled edge computing's expansion. This presents an opportunity for Artificial Intelligence (AI) to be embedded into these edge devices.
- Advances in AI Technology: The rapid progress made in AI, particularly deep learning and machine learning, has allowed it to be fully integrated into edge devices.
- Edge AI provides several benefits: it reduces latency, enhances security and privacy, increases dependability, and allows real-time data processing.
- Edge AI's Expanding Adoption in Industries: Edge AI is seeing increasing adoption across various industries such as healthcare, manufacturing, retail and transportation.
- Integration with 5G networks: Combining AI with 5G networks will enable real-time data processing and analysis, leading to increased efficiency and productivity.
- Overall, the AI for edge devices market is expected to experience steady growth over the coming years due to advances in technology and increasing adoption across various industries.
Regional Snapshot
The Artificial Intelligence for Edge Devices market is expected to experience rapid growth in regions such as North America, Europe, Asia Pacific and the Middle East & Africa. North America currently leads this space due to the increasing adoption of edge computing and IoT devices across industries like healthcare, retail and manufacturing. Asia Pacific will experience substantial expansion due to the presence of a large number of connected devices and increased investments in AI research & development.
Edge computing is becoming more and more popular due to its ability to process data closer to where it originates, decreasing latency and improving performance. AI for edge devices takes advantage of this trend to provide intelligent real-time insights.
Drivers
Edge computing is becoming more and more popular due to its ability to process data closer to where it originates, decreasing latency and improving performance. AI for edge devices takes advantage of this trend to provide intelligent real-time insights.
- Proliferation of Internet of Things (IoT) devices: As IoT adoption rates grow across various industries, so does the demand for AI at the edge. AI algorithms can be embedded into these devices to facilitate smart decision-making and predictive maintenance.
- Data Privacy & Security Fundamental: As more devices connect to the internet, data privacy and security have become even more crucial. AI for edge devices allows data processing close to home, decreasing the chance that sensitive information could be compromised during transmission.
- Advances in Hardware Technology: The introduction of powerful, energy-saving processors and sensors has allowed AI algorithms to be deployed on small, low-powered edge devices.
- A Growing Need for Real-Time Insights: With the explosion of data, there is an urgent need for timely insights to make informed decisions. AI on edge devices can analyze data in real-time and offer actionable insights without needing cloud connectivity.
- Edge Analytics on the Rise: Edge analytics is becoming an invaluable resource for businesses to analyze data and extract insights at the edge. AI for edge devices can improve accuracy in this analysis, leading to more precise outcomes.
- Overall, the AI for Edge Devices market is being driven by a demand for real-time insights, data privacy and security, edge computing, IoT devices, hardware advancements, and edge analytics.
Restraints
Artificial Intelligence (AI) for Edge Devices refers to the integration of AI algorithms and technologies into edge computing devices like smartphones, IoT sensors, and other gadgets that collect and process data locally. While AI offers many opportunities for innovation in this space as well as new applications, there are also several challenges that must be taken into account.
- Limited Processing Power: Edge devices typically lack processing power, meaning they may be unable to handle complex AI algorithms or processes. This could impact the performance and accuracy of AI applications running on edge devices.
- Limited Storage Capacity: Edge devices often lack adequate storage, which can restrict the amount of data that can be stored locally. This could negatively impact AI algorithms that require large datasets for proper computation.
- Security Concerns: Edge devices are frequently employed in sensitive applications like healthcare and financial services, making security an especially pressing concern. Any breaches or data leaks can have disastrous results. AI algorithms may also be vulnerable to attacks which could compromise both their integrity and performance.
- Privacy Aspects: AI algorithms on edge devices may collect and process sensitive data, such as personal information, location data, and other personally identifiable information. This poses privacy issues because users may feel hesitant to share their details with these devices.
- Compatibility Issues: AI algorithms may not be compatible with all edge devices, limiting their range of applications and use cases. This fragmentation in the market occurs as different devices require distinct AI algorithms and technologies.
- Cost: The development and deployment of AI for Edge Devices can be expensive, which may limit adoption and accessibility. This may be especially true for smaller businesses and startups that may not have the resources to invest in these technologies.
- Integration Complexity: Integrating AI algorithms into edge devices can be challenging and require specialized skills that may not be available to all developers. This could restrict the pool of developers who can work on these technologies, potentially slowing down development and adoption of AI for Edge Devices.
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Opportunities
- With the growing adoption of IoT devices and the requirement for real-time data processing, there is an increasing demand for AI on edge devices.
- Low cost, high performance processors make deploying AI models on edge devices much simpler.
- The expansion of 5G networks is anticipated to further accelerate the adoption of edge computing and AI on edge devices.
- The potential cost savings and improved efficiency that performing AI on edge devices instead of in the cloud presents another opportunity for this market.
Challenges
- Constructing AI algorithms that can run with limited computing resources and power constraints presents a major hurdle.
- Furthermore, safeguarding the security and privacy of data on edge devices remains another formidable obstacle.
- Due to a lack of standardization in the industry, it can be challenging to develop and deploy AI models across various devices and platforms.
- Furthermore, specialized hardware and software necessary for running AI on edge devices could pose another hurdle to adoption.
Recent Developments
- In 2021, NVIDIA unveiled their Jetson Nano 2GB Developer Kit – an accessible platform for developing AI on edge devices.
- Microsoft also recently unveiled their Azure Percept platform with hardware and software components designed specifically for creating and deploying AI models on edge devices.
- In 2020, Google unveiled their Coral AI platform – a collection of hardware and software components designed for creating and deploying AI on edge devices.
- Intel followed suit in 2020 with the OpenVINO toolkit, providing developers with the opportunity to optimize and deploy AI models across various edge devices.
Key Market Segments
Type
- Hardware
- Software
Application
- Automotive
- Consumer and Enterprise Robotics
- Drones
- Head-Mounted Displays
- Smart Speakers
- Mobile Phones
- PCs/Tablets
- Security Cameras
Key Market Players
- Alibaba
- Apple
- Arm
- Baidu
- CEVA Logistics
- Cambricon
- Horizon Robotics
- Intel
- Kneron
- MediaTek
- Mobileye
- Movidius
- Mythic
- NVIDIA
- Qualcomm
- Edge AI Hardware Enablers
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Report Scope
| Report Attribute | Details |
| The market size value in 2022 | USD 3.01 Bn |
| Revenue forecast by 2032 | USD 19.11 Bn |
| Growth Rate | CAGR Of 20.3% |
| Regions Covered | North America, Europe, Asia Pacific, Latin America, and Middle East & Africa, and Rest of the World |
| Historical Years | 2017-2022 |
| Base Year | 2022 |
| Estimated Year | 2023 |
| Short-Term Projection Year | 2028 |
| Long-Term Projected Year | 2032 |
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