The global AI on EDGE Semiconductor market was valued at US$ 2,718 million in 2023 and is anticipated to reach US$ 8,130 million by 2030, witnessing a CAGR of 16.54% during the forecast period 2024-2030.
The major global manufacturers of AI on EDGE Semiconductor include: NVIDIA, Intel, AMD Xilinx, Google, Qualcomm, NXP, ST, TI, Kneron, Inc., Hailo, etc. In 2023, the world’s top five vendors accounted for approximately 54% of the revenue.
Report Scope
This report aims to provide a comprehensive presentation of the global market for AI on EDGE Semiconductor, with both quantitative and qualitative analysis, to help readers develop business/growth strategies, assess the market competitive situation, analyze their position in the current marketplace, and make informed business decisions regarding AI on EDGE Semiconductor.
The AI on EDGE Semiconductor market size, estimations, and forecasts are provided in terms of output/shipments (K Units) and revenue ($ millions), considering 2023 as the base year, with history and forecast data for the period from 2019 to 2030. This report segments the global AI on EDGE Semiconductor market comprehensively. Regional market sizes, concerning products by Type, by Application, and by players, are also provided.
For a more in-depth understanding of the market, the report provides profiles of the competitive landscape, key competitors, and their respective market ranks. The report also discusses technological trends and new product developments.
The report will help the AI on EDGE Semiconductor manufacturers, new entrants, and industry chain related companies in this market with information on the revenues, production, and average price for the overall market and the sub-segments across the different segments, by company, by Type, by Application, and by regions.
Market Trends and Future Outlook
Emergence of AI at the Edge:
The adoption of AI at the edge is experiencing a notable surge, driven by the increasing demand for real-time processing and decision-making capabilities in IoT devices and applications. This trend is reshaping industries by enabling enhanced efficiency and responsiveness in data-driven operations.
Edge Computing’s Rising Prominence:
Edge computing is gaining traction due to its pivotal role in minimizing latency and optimizing overall system efficiency. By processing data closer to its source, edge computing reduces the need for data transmission to centralized servers, thereby improving response times and conserving bandwidth.
Strategic Partnerships and Collaborations:
Manufacturers are poised to engage in strategic partnerships and collaborations to capitalize on synergies and accelerate advancements in AI at the edge. These alliances enable companies to leverage each other’s strengths in AI on EDGE development, fostering innovation and market competitiveness.
Joint Ventures for Research and Development:
Joint ventures for research and development are anticipated as a strategic approach for manufacturers to stay competitive and address evolving market demands effectively. By pooling resources and expertise, companies can navigate the complexities of AI at the edge landscape, driving innovation and delivering cutting-edge solutions to meet customer needs.
Market Segmentation
By Company
- NVIDIA
- Intel
- AMD Xilinx
- Qualcomm
- NXP
- ST
- TI
- Kneron, Inc.
- Hailo
- Ambarella
- Hisilicon
- Cambricon
- Horizon Robotics
- Black Sesame Technologies
- Corerain
by Type
- Audio and Sound Processing
- Machine Vision
- Sensor Data Analysis
- Others
by Application
- Automotive
- Robotics
- Smart Manufacturing
- Smart City
- Security & Surveillance
- Others
Production by Region
- North America
- Europe
- China
- Japan
- South Korea
- China Taiwan
Consumption by Region
- North America
- U.S.
- Canada
- Asia-Pacific
- China
- Japan
- South Korea
- Southeast Asia
- India
- Europe
- Germany
- France
- U.K.
- Italy
- Russia
- Rest of Europe
- Latin America, Middle East & Africa
- Mexico
- Brazil
- Turkey
- Israel
- GCC Countries
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Taiwan Semiconductor Manufacturing Company (TSMC) sees a surge in orders for its cutting-edge 3nm chips from major technology companies like Apple, Intel, and AMD. This indicates a growing demand for advanced semiconductor technologies for AI on EDGE applications.
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Hailo, an Israeli semiconductor startup, is positioning itself to take advantage of the shift towards decentralized AI, where AI processing is moving from the cloud to the edge. The company sees a significant opportunity for newcomers to capture market share in this growing market.
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Sony Semiconductor Solutions Corporation has developed the AITRIOS edge AI sensing platform, which simplifies vision processing and inference, bringing AI closer to the edge.
Key Trends in the AI on Edge Semiconductor Market:
Advancements in Specialized Edge AI Chips: Tailored edge AI chips with compact form factors are gaining traction, prioritizing on-device inferencing over extensive neural network training. These chips emphasize efficiency, low latency, and cost-effectiveness.
Adoption of Purpose-Built Edge AI Accelerators: There’s a notable shift towards purpose-built edge AI accelerators, moving away from repurposing GPUs or FPGAs. These accelerators offer enhanced integration with sensors and are designed for scalability across diverse use cases.
Proliferation of Edge AI Across Various Industries: Edge AI is increasingly permeating consumer devices such as smartphones and home electronics, as well as industrial systems including retail kiosks, manufacturing machinery, and automotive applications.
Evolution of Edge AI Chip Architectures: Edge AI chip architectures are evolving from fixed-function designs to more flexible, programmable configurations. Concurrently, embedded board solutions are on the rise, offering greater versatility.
Advancements in Chip Manufacturing: Continuous progress in chip manufacturing techniques is evident, with a shift towards lower nanometer processes, package-on-package integration, and advancements in 3D stacking technologies.
Development of AI Model Optimization Techniques: Innovations in AI model optimization techniques such as pruning, quantization, and code generation are facilitating the compression of neural networks. This enables deployment on compact edge System-on-Chips (SoCs).
Emergence of Full-Stack Edge AI Solutions: Full-stack edge AI solutions are emerging, encompassing hardware, software tools, and pre-trained AI models offered by semiconductor vendors and startups. These comprehensive solutions streamline the development and deployment of edge AI applications.
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