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Posted via 11Press: The artificial intelligence for edge devices market encompasses the application of AI algorithms and tools to edge devices such as smartphones, IoT devices and other connected devices, enabling them to perform intelligent tasks without relying on a central cloud or a data center.
The market is expected to grow significantly due to the increasing adoption of edge computing and the proliferation of IoT devices. Edge AI is gaining popularity for its real-time decision-making capabilities while reducing latency and bandwidth requirements.
The global Artificial Intelligence for Edge Devices market size is projected to be worth around USD 19.11 billion by USD 3.01 billion in 2022-2032 and will grow at a CAGR of 20.3 during the forecast period of 2022-2032 % grow.
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Key Findings Growing Demand for Edge Computing: The explosion of IoT devices and the need for real-time processing have fueled the expansion of edge computing. This provides the ability to embed artificial intelligence (AI) into these edge devices. Advances in AI Technology: The rapid advances in AI, particularly deep learning and machine learning, have made it possible to fully integrate them into edge devices. Edge AI offers several benefits: it reduces latency, improves security and privacy, increases reliability, and enables real-time data processing. Increasing Adoption of Edge AI in Industries: Edge AI is seeing increasing adoption in various industries such as healthcare, manufacturing, retail, and transportation. Integration with 5G networks: The combination of AI with 5G networks will enable real-time data processing and analysis, leading to greater efficiency and productivity. Overall, the AI for edge devices market is expected to witness steady growth in the coming years owing to technological advancements and increasing adoption across various industries. Regional Snapshot
The artificial intelligence for edge devices market is expected to witness rapid growth in regions like North America, Europe, Asia-Pacific, and Middle East & Africa. North America is currently a leader in this space due to increasing adoption of edge computing and IoT devices in industries such as healthcare, retail and manufacturing. The Asia-Pacific region will experience significant expansion due to the presence of a large number of connected devices and increased investment in AI research and development.
Edge computing is growing in popularity due to its ability to process data closer to its point of origin, reducing latency and improving performance. AI for edge devices capitalizes on this trend to deliver real-time intelligent insights.
driver
Edge computing is growing in popularity due to its ability to process data closer to its point of origin, reducing latency and improving performance. AI for edge devices capitalizes on this trend to deliver real-time intelligent insights.
Internet of Things (IoT) device proliferation: As IoT adoption rates increase across industries, so does demand for AI at the edge. AI algorithms can be embedded into these devices to facilitate intelligent decision making and predictive maintenance. Privacy and security fundamental: As more devices connect to the internet, privacy and security become more important. AI for edge devices enables data processing close to home, reducing the likelihood that sensitive information could be compromised in transit. Advances in hardware technology: The advent of powerful, low-power processors and sensors has made it possible to deploy AI algorithms on small, low-power edge devices. A growing need for real-time insights: With the data explosion, there is an urgent need for timely insights to make informed decisions. AI on edge devices can analyze data in real time and provide actionable insights without requiring cloud connectivity. Edge Analytics on the Rise: Edge analytics is becoming an invaluable resource for organizations to analyze data and uncover insights at the edge. AI for edge devices can improve the accuracy of this analysis and lead to more precise results. Overall, the AI for edge devices market is driven by the demand for real-time insights, privacy and security, edge computing, IoT devices, hardware advancements, and edge analytics. restrictions
Artificial intelligence (AI) for edge devices refers to the integration of AI algorithms and technologies into edge computing devices such as smartphones, IoT sensors, and other devices that collect and process data locally. While AI offers many opportunities for innovation and new applications in this area, there are also some challenges that need to be considered.
Limited Processing Power: Edge devices typically lack processing power, which means they may not be able 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 sufficient storage, which can limit the amount of data that can be stored locally. This could negatively impact AI algorithms that require large data sets to compute properly. Security Concerns: Edge devices are often deployed in sensitive applications such as healthcare and financial services, making security a particularly pressing concern. Any breach or data leak can have catastrophic consequences. AI algorithms can also be vulnerable to attacks that could compromise both their integrity and performance. Privacy Considerations: AI algorithms on edge devices can collect and process sensitive data such as personal information, location data, and other personally identifiable information. This poses privacy issues as users may be reluctant to share their data with these devices. Compatibility issues: AI algorithms may not be compatible with all edge devices, limiting their scope and use cases. This market fragmentation occurs as different devices require different AI algorithms and technologies. Cost: Developing and deploying AI for edge devices can be expensive, which can limit adoption and accessibility. This is especially true for smaller companies 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 requires specialized skills that may not be available to all developers. This could limit the pool of developers who can work on these technologies and potentially slow the development and adoption of AI for edge devices.
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Opportunities With the increasing adoption of IoT devices and the need for real-time data processing, the demand for AI on edge devices is increasing. Inexpensive, high-performance processors make deploying AI models to edge devices much easier. The rollout of 5G networks is expected to further accelerate the adoption of edge computing and AI on edge devices. The potential cost savings and improved efficiencies that come from running AI on edge devices rather than in the cloud present another opportunity for this market. Challenges Designing AI algorithms that can run with limited computing resources and performance constraints represents a major hurdle. Additionally, maintaining the security and confidentiality of data on edge devices remains another formidable hurdle. Due to a lack of industry standardization, it can be difficult to develop and deploy AI models across different devices and platforms. Additionally, the specialized hardware and software required to run AI on edge devices could pose another hurdle to adoption. Recent Developments In 2021, NVIDIA introduced its Jetson Nano 2GB Developer Kit – an accessible platform for developing AI on edge devices. Microsoft also recently unveiled its Azure Percept platform with hardware and software components specifically designed for building and deploying AI models to edge devices. In 2020, Google unveiled its Coral AI platform – a collection of hardware and software components designed to build and deploy AI to edge devices. Intel followed suit in 2020 with the OpenVINO toolkit, offering developers the ability to tweak and deploy AI models across various edge devices. Important market segments
Type
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 Google 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 3.01B Revenue Forecast by 2032 USD 19.11B Growth Rate CAGR of 20.3% Regions Covered North America, Europe, Asia-Pacific, Latin America and Middle East & Africa and Rest of World Historical Years 2017-2022 Base year 2022 Estimated year 2023 Short term projection year 2028 Long term projected year 2032 Contact us
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Pramod Pawar
Pramod is an entrepreneur with a background in technology, web development and digital marketing. In the early years of his career, Pramod worked with various market research agencies. These ranged from small startup companies to large online marketing agencies. This gave him the opportunity to get in touch with big companies worldwide. He has over 10 years of market research and digital marketing experience.
