The Embedded AI Market is estimated to grow from USD 9.4 billion in 2023 to USD 18.0 billion by 2028, at a CAGR of 14.0% during the forecast period. Embedded AI is a system that integrates artificial intelligence (AI) technologies into embedded systems that are designed for specific tasks or functions. It involves deploying AI algorithms, models, and software directly on embedded devices, such as microcontrollers, system-on-chips (SoCs), and other hardware platforms.
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By offering, Services to register for the highest CAGR during the forecast period
The scope of the services segment comprises training and consulting, system integration and implementation, and support and maintenance. The services segment of the Embedded AI market is growing rapidly as businesses increasingly look to outsource the development and deployment of AI solutions. This is due to a number of factors, including the complexity of developing AI-powered applications, the need for specialized expertise, and the high cost of in-house development. Businesses looking to adopt AI should consider the services segment as a viable option for developing and deploying AI solutions.
By data type, numeric data to register for the highest market size during the forecast period
The increasing demand for real-time data processing and analysis boosts the demand for numeric data type in the embedded AI market. Numeric data plays a crucial role in the embedded AI market, forming the foundation for training, optimizing, and deploying AI models on embedded systems. Numeric data captured by sensors, cameras, or other sources in real-time serves as input to the deployed AI models, allowing them to process the data and generate actionable outputs promptly. This is crucial for robotics, autonomous vehicles, or industrial automation applications, where quick decision-making is required.
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Key players operating in the embedded AI market include Google (US), IBM (US), Microsoft (US), AWS (US), NVIDIA (US), Intel (US), Qualcomm (US), Arm (UK), AMD (US), MediaTek (Taiwan), Oracle (US), Salesforce (US), NXP (The Netherlands), Lattice (US), Octonion (Switzerland), NeuroPace (US), Siemens (Germany), HPE (US), LUIS Technology (Germany), Code Time Technologies (Canada), HiSilicon (China), VectorBlox (Canada), AU-Zone Technologies (Canada), STMicroelectronics (Switzerland), SenseTime (Hong Kong), Edge Impulse (US), Perceive (US), Eta Compute (US), SensiML (US), Syntiant (US), Graphcore (UK), and SiMa.ai (US). These embedded AI vendors have adopted various organic and inorganic strategies to sustain their positions and increase their global market shares.
IBM
IBM is a leading cloud solution providersthat engages in the establishment of integrated solutions that leverage information technology and knowledge of business processes. The company operates through the following segments: Cloud & Cognitive Software, Global Business Services, Global Technology Services, Systems, and Global Financing. The Cloud & Cognitive Software segment provides combined and secure cloud, data, and solutions to customers. The Global Business Services segment provides clients consulting, application management, and global process services. The Global Technology Services segment offers comprehensive IT infrastructure and platform services that create business value for clients. The Systems segment provides clients with innovative infrastructure platforms to help meet the requirements of hybrid cloud and enterprise AI workload. The Global Financing segment encompasses two primary businesses: financing, primarily conducted through IBM Credit LLC. IBM offers a range of Embedded AI offerings in the market, providing organizations with the tools and technologies to incorporate artificial intelligence capabilities directly into their devices and systems. .. IBM offers companies with the tools they need to incorporate AI capabilities into their embedded systems, such as IBM Watson Assistant, IBM Power Ai Vision, IBM Watson Studio, and IBM Watson IoT Platform. Moreover, these offerings from IBM provide organizations with the tools, platforms, and infrastructure necessary to develop, deploy, and manage embedded AI solutions. IBM’s Embedded AI offerings empower organizations to leverage AI capabilities directly within their devices and systems, enabling real-time inferencing, local data processing, and intelligent decision-making at the edge.
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Google continues to make significant research and development investments and seeks to develop new, innovative offerings and improve existing offerings across its businesses. The company also invested in technical infrastructure, including servers, network equipment, and data centers, to support the growth of the business in support of AI. In addition, acquisitions and strategic investments contribute to the breadth and depth of the company’s offerings, expand its expertise in engineering and other functional areas, and allow the company to build strong partnerships. For example, in September 2022, Google closed the acquisition of Mandiant to help expand its offerings in dynamic cyber defense and response. The company has expanded its offerings to include TensorFlow Lite, Coral, and ML Kit products. Additionally, Google has invested in artificial intelligence and machine learning technologies, which have the potential to transform multiple industries. Google has been investing in hardware development, such as the Coral Dev Board, to provide more powerful and efficient processing capabilities for AI. Google has also collaborated with industry partners and developers to create a more open and accessible ecosystem for embedded AI.
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