市場調查報告書
商品編碼
1470500
邊緣人工智慧市場:按處理器、組件、資料來源、最終用途和應用分類 - 2024-2030 年全球預測Edge Artificial Intelligence Market by Processor (ASIC, CPU, GPU), Component (Services, Solution), Source, End-Use, Application - Global Forecast 2024-2030 |
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邊緣人工智慧(AI)市場規模預計2023年為10.6億美元,2024年達到13.1億美元,預計2030年將達到49.8億美元,複合年成長率為24.71%。
邊緣人工智慧是指人工智慧演算法在硬體設備上本地處理的系統。設備即時處理資料並做出決策,無需依賴雲端或集中式資料中心。這種去中心化方法可以透過將先進的人工智慧和機器學習功能直接整合到智慧型手機、物聯網 (IoT) 設備和自動駕駛汽車等邊緣設備中來實現。各行業對低延遲處理和即時決策能力的需求不斷成長,正在推動邊緣人工智慧技術的開發和採用。物聯網設備的普及以及在不增加網路頻寬負擔的情況下從源頭處理大量資料的需求進一步推動了對此類創新解決方案的需求,並擴大了邊緣人工智慧市場的範圍。然而,在邊緣設備上部署和維護人工智慧模型的複雜性以及資料安全和隱私問題給市場帶來了挑戰。儘管面臨挑戰,醫療、汽車和製造領域的智慧應用激增為邊緣人工智慧帶來了巨大的機會。半導體技術的進步和人工智慧研究投資的增加可能會帶來更強大、更有效率的邊緣人工智慧解決方案。
主要市場統計 | |
---|---|
基準年[2023] | 10.6億美元 |
預測年份 [2024] | 13.1億美元 |
預測年份 [2030] | 49.8億美元 |
複合年成長率(%) | 24.71% |
處理器:由於能源效率和效能,對 ASIC 的偏好增加
ASIC 是為特定用途而不是通用目的而設計的。在邊緣人工智慧的背景下,ASIC 提供高效率,並針對特定人工智慧演算法和模型進行了最佳化。當特定任務的能源效率和高效能很重要時,ASIC 是首選。 ASIC 非常適合需要即時處理的大容量嵌入式設備,例如物聯網設備、自動駕駛汽車和智慧型手機。 CPU是電腦的重要組成部分,負責大部分處理任務。在Edge AI中,CPU可以被認為是更通用的處理器。當彈性很重要時通常會使用 CPU。 CPU 具有執行多項任務的能力,使其適合需要複雜決策能力且不一定需要 ASIC 或 GPU 高速處理的應用。儘管 GPU 是為渲染圖形而設計的,但它們的平行處理能力有助於加快深度學習任務的速度。 GPU 是機器學習訓練和推理任務的理想處理器,因為它們可以同時處理多個進程。 GPU 非常適合視訊分析、訓練 AI 模型以及平行處理可顯著減少運算時間的任何應用。
資料來源:基於邊緣的生物辨識系統減少反應時間並減少網路頻寬負載
生物辨識資料包括收集和分析能夠唯一識別個人的身體和行為屬性,例如指紋、臉部辨識、虹膜掃描和語音模式。在邊緣人工智慧的背景下,在本地處理生物辨識資料可以減少延遲,提高隱私性,並確保即使在連接間歇性的情況下也能正常運作。行動資料包括行動裝置產生的大量資訊,例如位置資訊、應用程式使用統計數據和用戶行為洞察。利用邊緣AI處理行動資料可以顯著增強服務個人化和即時決策能力。感測器資料是指嵌入設備和環境中的實體感測器的輸出,捕捉溫度、濕度、振動和運動等各種指標。邊緣人工智慧會立即處理這些資料,以實現高效的營運回應。語音辨識技術使設備能夠理解和處理人類語音命令,並將其轉換為可操作的資料。與邊緣人工智慧的整合有助於無縫互動並減少對雲端處理的依賴。影片和影像識別分析視覺內容以識別物件、臉部、場景和活動。邊緣人工智慧透過直接在相機和智慧型手機等設備上處理內容,支援監控、零售分析和自動駕駛等應用,加速了這項任務。
最終用途:政府和公共部門更多地採用邊緣人工智慧,並專注於服務交付和資料安全
汽車產業擴大將邊緣人工智慧解決方案應用於各種應用,包括自動駕駛、預測性維護和改善用戶體驗。邊緣人工智慧使汽車能夠透過本地處理資料來快速響應不斷變化的環境,消除可能危及乘客安全的延遲。能源和公共產業正在採用邊緣人工智慧來管理電網運作、最佳化能源流並提供基礎設施的預測性維護。對能源發行網路的營運效率和進階監控的需求至關重要,邊緣人工智慧可以幫助公共產業公司根據瞬時資料做出即時決策。在政府和公共部門,邊緣人工智慧正被用於智慧城市計畫、公共和交通系統。此細分市場的需求是改善服務交付,同時確保公民隱私和安全。醫療領域透過增強的病患監測、醫學影像分析和醫院物流受益於邊緣人工智慧。對邊緣人工智慧的需求源於在局部快速處理大量敏感醫療資料以進行及時決策的緊迫性。製造業中的邊緣人工智慧旨在品管、預測性維護和供應鏈最佳化。對這項技術的需求尤其受到工廠車間產生的大量資料點的驅動,這些數據點需要即時分析以提高生產率和安全性。通訊業者正在使用邊緣人工智慧來最佳化網路、改善客戶體驗和預測分析。
應用:將邊緣AI引入智慧穿戴設備,可以實現更準確的資料分析和更快的處理。
存取管理中的邊緣人工智慧包括生物識別、安全系統和智慧鎖技術。加強私營和公共部門安全通訊協定的需求正在推動人們對這些解決方案的偏好。邊緣人工智慧可實現即時資料處理,減少延遲並提高決策速度。自動駕駛汽車(AV)中的邊緣人工智慧是指使用本地處理的人工智慧演算法來即時執行路徑規劃、物件偵測和決策等任務。由於研究和開發的增加以及對更安全道路的推動,自動駕駛汽車中的邊緣人工智慧變得越來越受歡迎。邊緣人工智慧驅動的能源管理涉及最佳化能源使用並降低商業和工業環境中的營運成本。這種偏好源自於對永續和節能營運的追求。由邊緣人工智慧驅動的精密農業可實現作物監測和土壤狀況分析等智慧農業技術。對糧食安全和永續農業實踐日益成長的需求正在提高其偏好。由邊緣人工智慧支援的智慧型穿戴裝置包括健身追蹤器和醫療監測設備,可即時洞察個人健康指標。消費者對個人化健康資料和便利性的需求正在推動這些設備的擴展和偏好。遙測中的邊緣人工智慧從航太和汽車等領域的遠端或無法存取的位置收集和處理資料。對邊緣人工智慧遙測的偏好是由資料傳輸中即時資料處理的需求所驅動的。具有邊緣人工智慧的視訊監控用於零售和公共等領域的即時威脅檢測和分析。邊緣人工智慧在監控系統中受到青睞,因為它可以有效減少誤報並提供即時分析。
區域洞察
由於雲端基礎技術的穩定採用和物聯網設備的日益普及,美洲的邊緣人工智慧市場正經歷強勁成長。北美尤其是技術創新中心,老字型大小企業不斷擴大其邊緣人工智慧解決方案的產品範圍。在歐洲、中東和非洲,邊緣人工智慧市場充滿活力且多元。在歐洲,《一般資料保護規範》(GDPR) 等嚴格的隱私法規正在推動向本地資料處理的轉變,並刺激邊緣人工智慧技術的發展。在中東,人工智慧在邊緣的使用正在取得進展,以加強智慧城市計畫以及石油和天然氣業務。同時,非洲的投資正在成長,特別是在農業技術和醫療保健等領域,邊緣人工智慧可以顯著提高效率和可近性。由於中國、韓國和日本人工智慧技術滲透率的提高以及政府的支持,亞太地區顯示出巨大的潛力,預計將在邊緣人工智慧市場中呈現最高的成長率。亞太地區的大型製造地擴大採用人工智慧邊緣運算來實現即時流程最佳化。此外,該地區蓬勃發展的消費性電子產業為將邊緣人工智慧融入消費性設備提供了肥沃的土壤。
FPNV定位矩陣
FPNV定位矩陣對於評估邊緣人工智慧市場至關重要。我們檢視與業務策略和產品滿意度相關的關鍵指標,以對供應商進行全面評估。這種深入的分析使用戶能夠根據自己的要求做出明智的決策。根據評估,供應商被分為四個成功程度不同的像限。最前線 (F)、探路者 (P)、利基 (N) 和重要 (V)。
市場佔有率分析
市場佔有率分析是一個綜合工具,可以對邊緣人工智慧市場供應商的現狀進行深入而深入的研究。全面比較和分析供應商在整體收益、基本客群和其他關鍵指標方面的貢獻,以便更好地了解公司的績效及其在爭奪市場佔有率時面臨的挑戰。此外,該分析還提供了對該細分市場競爭特徵的寶貴見解,包括在研究基準年觀察到的累積、碎片化主導地位和合併特徵等因素。詳細程度的提高使供應商能夠做出更明智的決策並制定有效的策略,從而在市場上獲得競爭優勢。
1. 市場滲透率:提供有關主要企業所服務的市場的全面資訊。
2. 市場開拓:我們深入研究利潤豐厚的新興市場,並分析其在成熟細分市場的滲透率。
3. 市場多元化:包括新產品發布、開拓地區、最新發展和投資的詳細資訊。
4. 競爭評估和情報:對主要企業的市場佔有率、策略、產品、認證、監管狀況、專利狀況和製造能力進行全面評估。
5. 產品開發與創新:包括對未來技術、研發活動和突破性產品開發的智力見解。
1.邊緣人工智慧市場的市場規模與預測是多少?
2. 在邊緣人工智慧市場預測期內,我們應該考慮投資哪些產品和應用?
3.邊緣人工智慧市場的技術趨勢和法規結構是什麼?
4.邊緣人工智慧市場主要廠商的市場佔有率為何?
5. 進入邊緣人工智慧市場的合適形式和策略手段是什麼?
[194 Pages Report] The Edge Artificial Intelligence Market size was estimated at USD 1.06 billion in 2023 and expected to reach USD 1.31 billion in 2024, at a CAGR 24.71% to reach USD 4.98 billion by 2030.
Edge artificial intelligence (AI) refers to a system where AI algorithms are processed locally on a hardware device. The device undertakes data processing and decision-making in real time without relying on the cloud or centralized data centers. This decentralized approach is achievable through the integration of advanced AI and machine learning capabilities directly into edge devices, such as smartphones, IoT (Internet of Things) devices, and autonomous vehicles. Increased demand for low-latency processing and real-time decision-making capabilities in various industries are driving the development and adoption of edge AI technology. The proliferation of IoT devices and the need to process vast amounts of data at the source without overloading network bandwidth further increases the demand for these innovative solutions, thus expanding the scope of the edge artificial intelligence market. However, concerns over data security and privacy, alongside the complexity of deploying and maintaining AI models on edge devices, present challenges for the market. Despite the challenges, the surge in intelligent applications across healthcare, automotive, and manufacturing sectors presents significant opportunities for edge AI. Advancements in semiconductor technologies and increased investments in AI research can lead to more powerful and efficient edge AI solutions.
KEY MARKET STATISTICS | |
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Base Year [2023] | USD 1.06 billion |
Estimated Year [2024] | USD 1.31 billion |
Forecast Year [2030] | USD 4.98 billion |
CAGR (%) | 24.71% |
Processor: Increasing preference of ASIC due to its energy efficiency and high performance
An ASIC is designed for a particular use rather than for general-purpose use. In the context of Edge AI, ASICs offer high efficiency and are optimized for specific AI algorithms and models. ASICs are preferred in instances where energy efficiency and high performance for specific tasks are critical. They are ideal for high-volume, embedded devices that require real-time processing, such as IoT devices, autonomous vehicles, and smartphones. The CPU is an important component of a computer that takes out most of the processing tasks. In Edge AI, CPUs can be seen as a more general-purpose processor. CPUs are typically used when flexibility is important. They are competent in performing multiple tasks and are suitable for applications that require complex decision-making capabilities, which do not necessarily need the high-speed processing of ASICs or GPUs. GPUs are designed to render graphics but have become beneficial in accelerating deep learning tasks due to their parallel processing capabilities. GPUs are the go-to processors for machine learning training and inference tasks due to their ability to handle multiple operations simultaneously. They are ideal for video analytics, AI model training, and any application where parallel processing can significantly reduce computation times.
Source: Edge-based biometric systems provide faster response times and reduce bandwidth load on networks
Biometric data involves the collection and analysis of physical and behavioral attributes that enable the unique identification of individuals, including fingerprints, facial recognition, iris scans, and voice patterns. In the context of edge AI, processing biometric data locally reduces latency, enhances privacy, and ensures operation even with intermittent connectivity. Mobile data encompasses the vast amount of information generated by mobile devices, such as location data, app usage statistics, and user behavior insights. Leveraging edge AI for processing mobile data can greatly enhance the personalization of services and real-time decision-making capacity. Sensor data refers to the output from physical sensors embedded in devices or environments, capturing a range of indicators such as temperature, humidity, vibration, and motion. Edge AI enables the immediate processing of this data for efficient operational responses. Speech recognition technology enables devices to understand and process human voice commands and convert them into actionable data. When integrated with edge AI, it facilitates seamless interaction and reduces the dependency on cloud processing. Video and image recognition involves analyzing visual content to identify objects, faces, scenes, and activities. Edge AI accelerates this task by processing content directly on devices, including cameras and smartphones, thus supporting applications such as surveillance, retail analytics, and autonomous driving.
End-Use: Rising adoption of edge AI by government and public sector, emphasizing service delivery and data security
The automotive industry is increasingly integrating edge AI solutions for various applications such as autonomous driving, predictive maintenance, and enhanced user experiences. Edge AI enables cars to respond quickly to changing environments by processing data locally, eliminating delays that could potentially compromise passenger safety. Energy and utilities employ edge AI for managing grid operations, optimizing energy flow, and predictive maintenance of infrastructure. The need for operational efficiency and advanced monitoring of energy distribution networks is paramount, as edge AI helps utilities make real-time decisions based on instantaneous data. In the government and public sector, edge AI is utilized for smart city initiatives, public safety, and transportation systems. The need in this sector is to improve service delivery while ensuring the privacy and security of the citizens. The healthcare sector benefits from edge AI through enhanced patient monitoring, medical imaging analysis, and in-hospital logistics. The need for edge AI stems from the urgency to process large volumes of sensitive health data quickly and locally for timely decision-making. Edge AI in manufacturing is aimed at quality control, predictive maintenance, and supply chain optimization. The need for this technology is particularly acute due to the large quantity of data points generated on the factory floor that require immediate analysis to improve productivity and safety. Telecom operators use edge AI for network optimization, customer experience enhancement, and predictive analytics.
Application: Deployment of edge AI in smart wearables to offer more accurate data analysis and faster processing
Edge AI in access management encompasses biometric authentication, security systems, and smart lock technologies. The need for enhanced security protocols in both private and public sectors drives preference for these solutions. Edge AI allows real-time data processing, thereby reducing latency and improving decision-making speed. Edge AI in autonomous vehicles (AVs) refers to the use of AI algorithms processed locally to perform tasks such as path planning, object detection, and decision-making in real time. The increase in R&D and the push for safer roads give edge AI in AVs a growing preference. Energy management utilizing edge AI involves optimizing energy usage and reducing operational costs in commercial and industrial settings. Its preference stems from the pursuit of sustainable and energy-efficient operations. Precision agriculture with edge AI allows for smart farming techniques, including crop monitoring and soil condition analysis. The rising need for food security and sustainable agricultural practices enhances its preference. Smart wearables using edge AI include fitness trackers and medical monitoring devices that provide real-time insights into personal health metrics. Consumer demand for personalized health data and convenience drives the expansion and preference for these devices. Edge AI in telemetry involves collecting and processing data from remote or inaccessible areas in fields such as aerospace and automotive. Preferences for edge AI telemetry are fueled by the need for real-time data processing in data transmission. Video surveillance with edge AI is used for real-time threat detection and analysis in sectors such as retail and public security. The preference for edge AI in surveillance systems is due to their effectiveness in reducing false alarms and providing immediate analysis.
Regional Insights
The market for edge artificial intelligence (AI) in the Americas is experiencing robust growth, driven by the robust adoption of cloud-based technologies and the increasing prevalence of IoT devices. North America, in particular, is a hub for technological innovation, with well-established players expanding their offerings in edge AI solutions. In the EMEA region, the edge AI market is marked by a dynamic and diverse landscape. Europe's strict privacy regulations, such as the General Data Protection Regulation (GDPR), are catalyzing the shift toward local data processing, thus fueling the growth of edge AI technologies. The Middle East is leveraging AI at the edge for smart city initiatives and to enhance oil and gas operations. Meanwhile, investments in Africa are growing, particularly in areas including agritech and healthcare, where edge AI can greatly improve efficiency and accessibility. The APAC region demonstrates significant potential and is expected to witness the highest growth rate in the edge AI market, owing to the increasing penetration of AI technologies and government support in China, South Korea, and Japan. APAC's large manufacturing base is actively incorporating AI edge computing for real-time process optimization. Furthermore, the region's burgeoning consumer electronics sector provides a fertile ground for embedding edge AI into consumer devices.
FPNV Positioning Matrix
The FPNV Positioning Matrix is pivotal in evaluating the Edge Artificial Intelligence Market. It offers a comprehensive assessment of vendors, examining key metrics related to Business Strategy and Product Satisfaction. This in-depth analysis empowers users to make well-informed decisions aligned with their requirements. Based on the evaluation, the vendors are then categorized into four distinct quadrants representing varying levels of success: Forefront (F), Pathfinder (P), Niche (N), or Vital (V).
Market Share Analysis
The Market Share Analysis is a comprehensive tool that provides an insightful and in-depth examination of the current state of vendors in the Edge Artificial Intelligence Market. By meticulously comparing and analyzing vendor contributions in terms of overall revenue, customer base, and other key metrics, we can offer companies a greater understanding of their performance and the challenges they face when competing for market share. Additionally, this analysis provides valuable insights into the competitive nature of the sector, including factors such as accumulation, fragmentation dominance, and amalgamation traits observed over the base year period studied. With this expanded level of detail, vendors can make more informed decisions and devise effective strategies to gain a competitive edge in the market.
Key Company Profiles
The report delves into recent significant developments in the Edge Artificial Intelligence Market, highlighting leading vendors and their innovative profiles. These include Adlink Technology, Inc., Amazon Web Services Inc., Anagog Ltd., BrainChip Holdings Ltd., Cato Networks, Ltd., ClearBlade, Inc., Cloudera, Inc., Edge Intelligence Software, Inc. by Adapdix, Inc., EdgeConneX, EdgeIQ, Eta Compute Inc., Google LLC by Alphabet Inc., Gorilla Technology Inc., Hewlett Packard Enterprise Company, Intel Corporation, International Business Machines Corporation, Johnson Controls International PLC, Lenovo Group Ltd., Microsoft Corporation, Nutanix, Inc., Octonion SA, Saguna Consulting Services LLC, Synaptics Incorporated, Tata Elxsi Limited, TIBCO Software Inc. by Cloud Software Group, Inc., Valores Corporativos Softtek, S.A. de C.V., and Vapor IO.
Market Segmentation & Coverage
1. Market Penetration: It presents comprehensive information on the market provided by key players.
2. Market Development: It delves deep into lucrative emerging markets and analyzes the penetration across mature market segments.
3. Market Diversification: It provides detailed information on new product launches, untapped geographic regions, recent developments, and investments.
4. Competitive Assessment & Intelligence: It conducts an exhaustive assessment of market shares, strategies, products, certifications, regulatory approvals, patent landscape, and manufacturing capabilities of the leading players.
5. Product Development & Innovation: It offers intelligent insights on future technologies, R&D activities, and breakthrough product developments.
1. What is the market size and forecast of the Edge Artificial Intelligence Market?
2. Which products, segments, applications, and areas should one consider investing in over the forecast period in the Edge Artificial Intelligence Market?
3. What are the technology trends and regulatory frameworks in the Edge Artificial Intelligence Market?
4. What is the market share of the leading vendors in the Edge Artificial Intelligence Market?
5. Which modes and strategic moves are suitable for entering the Edge Artificial Intelligence Market?