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市場調查報告書
商品編碼
1899908
邊緣人工智慧硬體市場規模、佔有率和成長分析(按設備、功耗、處理器、功能、垂直產業和地區分類)-2026-2033年產業預測Edge AI Hardware Market Size, Share, and Growth Analysis, By Device (Smartphones, Surveillance), By Power Consumptions (Less Than 1 W, 1-3 W), By Processor, By Function, By Vertical, By Region - Industry Forecast 2026-2033 |
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預計到 2024 年,邊緣 AI 硬體市場規模將達到 281.5 億美元,到 2025 年將成長至 331.4 億美元,到 2033 年將成長至 1220.5 億美元,在預測期(2026-2033 年)內,複合年成長率為 17.7%。
邊緣運算和互聯設備的日益普及推動了對邊緣人工智慧硬體需求的成長。對即時數據處理的需求,以及不斷擴展的物聯網 (IoT) 環境,為邊緣人工智慧硬體供應商開闢了新的機會。對能源效率的重視和硬體技術的進步預計將支撐市場的長期成長。自主技術的日益融合和人工智慧 (AI) 演算法的改進進一步刺激了對邊緣人工智慧硬體的需求。此外,對人工智慧專用硬體研發投入的增加也提升了市場的潛力。然而,整合複雜性、熟練專業人員短缺、高階任務的高能耗以及資料安全和隱私問題等挑戰,可能會在短期內阻礙整體需求的成長。
邊緣人工智慧硬體市場促進因素
各種應用對快速數據處理和低延遲的需求日益成長,預計將推動邊緣人工智慧硬體的銷售。自動駕駛汽車、工業自動化和智慧城市等關鍵領域需要即時決策,因此邊緣人工智慧硬體對其成功至關重要。各行業追求更高的效率、安全性和功能性,對即時處理能力的需求也至關重要,這將顯著推動邊緣人工智慧硬體市場的成長。隨著這些應用的不斷發展和擴展,預計它們將繼續在塑造邊緣人工智慧硬體解決方案的未來發揮主導作用。
邊緣人工智慧硬體市場面臨的限制因素
邊緣人工智慧硬體市場面臨嚴峻挑戰,主要原因是缺乏設計和運維先進邊緣人工智慧硬體解決方案所需的熟練工程師。隨著時間的推移,這種專業人才的匱乏可能會阻礙邊緣人工智慧硬體產品的銷售和成長。在全球邊緣人工智慧硬體市場格局中,新興市場預計受熟練勞動力短缺的影響將比已開發國家更為顯著。隨著企業尋求在該領域進行創新和擴張,合格人才的供應將在決定邊緣人工智慧技術的整體成功和發展方面發揮關鍵作用。
邊緣人工智慧硬體市場趨勢
邊緣人工智慧硬體市場正經歷著一個顯著的趨勢:微型機器學習(TinyML)的興起。 TinyML強調在超低功耗邊緣裝置上部署機器學習模型。這項創新不僅透過實現更靠近資料來源的即時處理來增強邊緣設備的功能,而且還顯著降低了消費量和延遲。鑑於TinyML在各行各業的變革性應用,邊緣人工智慧硬體公司正在增加對TinyML演算法和模型的投資。預計這一轉變將進一步推動邊緣人工智慧硬體產業的發展,促進收入成長,並加劇市場參與企業之間的競爭格局。
Edge AI Hardware Market size was valued at USD 28.15 Billion in 2024 and is poised to grow from USD 33.14 Billion in 2025 to USD 122.05 Billion by 2033, growing at a CAGR of 17.7% during the forecast period (2026-2033).
The escalating deployment of edge and connected devices is driving heightened demand for edge AI hardware. The necessity for real-time data processing, coupled with the expanding Internet of Things (IoT) landscape, is opening new avenues for edge AI hardware providers. A strong focus on energy efficiency and advancements in hardware technology are anticipated to support long-term market growth. The increasing integration of autonomous technologies and improvements in artificial intelligence (AI) algorithms further stimulate the demand for edge AI hardware. Additionally, rising investments in AI-specific hardware development enhance market potential. However, challenges such as integration complexities, a shortage of skilled professionals, high energy consumption for advanced tasks, and concerns regarding data security and privacy may hinder overall demand in the near future.
Top-down and bottom-up approaches were used to estimate and validate the size of the Edge AI Hardware market and to estimate the size of various other dependent submarkets. The research methodology used to estimate the market size includes the following details: The key players in the market were identified through secondary research, and their market shares in the respective regions were determined through primary and secondary research. This entire procedure includes the study of the annual and financial reports of the top market players and extensive interviews for key insights from industry leaders such as CEOs, VPs, directors, and marketing executives. All percentage shares split, and breakdowns were determined using secondary sources and verified through Primary sources. All possible parameters that affect the markets covered in this research study have been accounted for, viewed in extensive detail, verified through primary research, and analyzed to get the final quantitative and qualitative data.
Edge AI Hardware Market Segments Analysis
Global Edge AI Hardware Market is segmented by Device, Power Consumptions, Processor, Function, Vertical and region. Based on Device, the market is segmented into Smartphones, Surveillance, Robots, Wearables, Edge Servers, Smart Speakers, Automobiles and Other Devices. Based on Power Consumptions, the market is segmented into Less Than 1 W, 1-3 W, 3-5 W, 5-10 W and More Than 10 W. Based on Processor, the market is segmented into Central Processing Units, Graphics Processing Units, Application Specific Integrated Circuits and Other Processors. Based on Function, the market is segmented into Training and Inference. Based on Vertical, the market is segmented into Consumer Electronics, Smart Homes, Automotive & Transportation, Government, Healthcare, Industrial, Aerospace & Defense, Construction and Other verticals. Based on region, the market is segmented into North America, Europe, Asia Pacific, Latin America and Middle East & Africa.
Driver of the Edge AI Hardware Market
The increasing need for swift data processing without significant delays across various applications is expected to enhance the sales of edge AI hardware. Critical domains such as autonomous vehicles, industrial automation, and smart city initiatives require instantaneous decision-making, making edge AI hardware essential for their success. This demand for real-time capabilities will significantly fuel the growth of the edge AI hardware market, as it becomes indispensable for industries aiming to improve efficiency, safety, and functionality. As these applications evolve and expand, they will continue to be a driving force in shaping the future landscape of edge AI hardware solutions.
Restraints in the Edge AI Hardware Market
The Edge AI Hardware market faces a significant challenge due to a shortage of skilled professionals needed for the design and operation of sophisticated edge AI hardware solutions. This lack of expertise is likely to hinder the sales and growth of edge AI hardware products over time. Emerging markets are anticipated to experience a more pronounced impact from this skilled labor deficit compared to their developed counterparts in the global landscape of edge AI hardware. As companies strive to innovate and expand in this sector, the availability of qualified personnel will play a crucial role in determining the overall success and advancement of edge AI technology.
Market Trends of the Edge AI Hardware Market
The Edge AI Hardware market is experiencing a notable trend towards the emergence of Tiny Machine Learning (TinyML), which emphasizes the deployment of machine learning models on ultra-low-power edge devices. This innovation not only enhances the functionality of edge devices by enabling real-time data processing closer to the source but also significantly reduces energy consumption and latency. Edge AI hardware companies are increasingly investing in TinyML algorithms and models, recognizing the potential for transformative applications across various industries. As a result, this shift is set to elevate the edge AI hardware sector, driving revenue growth and fostering a competitive landscape for market participants.