Global Edge AI Hardware Market report from Global Insight Services is the single authoritative source of intelligence on Edge AI Hardware Market. The report will provide you with analysis of impact of latest market disruptions such as Russia-Ukraine war and Covid-19 on the market. Report provides qualitative analysis of the market using various frameworks such as Porters’ and PESTLE analysis. Report includes in-depth segmentation and market size data by categories, product types, applications, and geographies. Report also includes comprehensive analysis of key issues, trends and drivers, restraints and challenges, competitive landscape, as well as recent events such as M&A activities in the market.
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Edge AI hardware is a type of AI hardware that is designed for use in edge computing applications. Edge AI hardware is typically used to perform AI-related tasks such as image recognition, object detection, and voice recognition. Edge AI hardware is often used in conjunction with sensors and cameras to provide real-time data processing and analysis.
Key Trends
Over the past few years, there has been a growing interest in artificial intelligence (AI) and its potential to transform a wide range of industries. One area that has seen particularly rapid development is edge AI, which refers to the use of AI algorithms on devices at the edge of the network, such as sensors, cameras, and other IoT devices.
One of the key trends in edge AI hardware is the development of new types of processors that are specifically designed for AI applications. These include GPUs, FPGAs, and ASICS. GPUs are particularly well-suited for deep learning applications, which require large amounts of data processing and parallel computing. FPGAs are also suitable for deep learning, but they offer the added benefit of being programmable, which makes them more flexible and easier to update. ASICS are purpose-built chips that offer the highest performance but are also the most expensive.
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Another trend is the use of cloud computing to supplement or replace on-device AI processing. Cloud-based AI services can provide the computational power needed for complex applications, and they offer the added benefit of being able to update the algorithms used without having to replace the hardware.
Finally, there is a trend towards using open source hardware and software for edge AI. This includes platforms such as the Raspberry Pi, which is a low-cost credit-card sized computer that can be used for a variety of applications, and TensorFlow, an open source software library for machine learning. Using open source hardware and software makes it easier to develop and deploy AI applications, and it also helps to lower the barrier to entry.
Key Drivers
The key drivers of Edge AI Hardware market are the increasing demand for AI-enabled devices, the need for real-time insights, the need for enhanced security, and the need for efficient data processing.
AI-enabled devices are becoming increasingly popular as they offer a number of advantages over traditional devices. They are able to provide enhanced security, real-time insights, and efficient data processing. As a result, the demand for AI-enabled devices is expected to increase in the coming years.
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The need for real-time insights is another key driver of the Edge AI Hardware market. Traditional devices are unable to provide real-time insights due to the delay in data processing. However, AI-enabled devices are able to provide real-time insights as they can process data in real-time.
The need for enhanced security is another key driver of the Edge AI Hardware market. AI-enabled devices are equipped with advanced security features that can protect data from being accessed by unauthorized individuals. In addition, AI-enabled devices can also detect and prevent cyber-attacks.
The need for efficient data processing is another key driver of the Edge AI Hardware market. AI-enabled devices are able to process data more efficiently than traditional devices. As a result, the demand for AI-enabled devices is expected to increase in the coming years.
Key Market Segments
The edge AI hardware market bifurcated on the basis of component, device type, end-user and region. On the basis of component, it is segmented into processor, memory, sensor, and others. By device type it is categorized into smartphones, robots, surveillance cameras, and others. By end-user, it is spread across consumer electronics, smart home, automotive, and others. Region-wise, it is studied across North America, Europe, Asia-Pacific, and rest of the World.
Key Market Players
The edge AI hardware market report includes players such as Samsung Electronics, IBM, Micron Technology, Xilinx, Google, Microsoft, AMD, Intel Corporation, MediaTek Inc., and Apple.
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