市场调查报告书
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1470500
边缘人工智慧市场:按处理器、组件、资料来源、最终用途和应用划分 - 2024-2030 年全球预测Edge Artificial Intelligence Market by Processor (ASIC, CPU, GPU), Component (Services, Solution), Source, End-Use, Application - Global Forecast 2024-2030 |
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边缘人工智慧(AI)市场规模预计2023年为10.6亿美元,2024年达到13.1亿美元,预计2030年将达到49.8亿美元,复合年增长率为24.71%。
边缘人工智慧是指人工智慧演算法在硬体设备上本地处理的系统。设备即时处理资料并做出决策,无需依赖云端或集中式资料中心。这种去中心化方法可以透过将先进的人工智慧和机器学习功能直接整合到智慧型手机、物联网 (IoT) 设备和自动驾驶汽车等边缘设备中来实现。各行业对低延迟处理和即时决策能力的需求不断增长,正在推动边缘人工智慧技术的开发和采用。物联网设备的普及以及在不增加网路频宽负担的情况下从源头处理大量资料的需求进一步推动了对此类创新解决方案的需求,并扩大了边缘人工智慧市场的范围。然而,在边缘设备上部署和维护人工智慧模型的复杂性以及资料安全和隐私问题给市场带来了挑战。儘管面临挑战,医疗、汽车和製造领域的智慧应用激增为边缘人工智慧带来了巨大的机会。半导体技术的进步和人工智慧研究投资的增加可能会带来更强大、更有效率的边缘人工智慧解决方案。
主要市场统计 | |
---|---|
基准年[2023] | 10.6亿美元 |
预测年份 [2024] | 13.1亿美元 |
预测年份 [2030] | 49.8亿美元 |
复合年增长率(%) | 24.71% |
处理器:由于能源效率和效能,对 ASIC 的偏好增加
ASIC 是为特定用途而不是通用目的而设计的。在边缘人工智慧的背景下,ASIC 提供高效率,并针对特定人工智慧演算法和模型进行了最佳化。当特定任务的能源效率和高效能很重要时,ASIC 是首选。 ASIC 非常适合需要即时处理的大容量嵌入式设备,例如物联网设备、自动驾驶汽车和智慧型手机。 CPU是电脑的重要组成部分,负责大部分处理任务。在Edge AI中,CPU可以被认为是更通用的处理器。当弹性很重要时通常会使用 CPU。 CPU 具有执行多项任务的能力,使其适合需要复杂决策能力且不一定需要 ASIC 或 GPU 高速处理的应用。儘管 GPU 是为渲染图形而设计的,但它们的平行处理能力有助于加快深度学习任务的速度。 GPU 是机器学习训练和推理任务的理想处理器,因为它们可以同时处理多个进程。 GPU 非常适合视讯分析、训练 AI 模型以及平行处理可显着减少运算时间的任何应用。
资料来源:基于边缘的生物辨识系统减少反应时间并减少网路频宽负载
生物辨识资料包括收集和分析能够唯一识别个人的身体和行为属性,例如指纹、脸部辨识、虹膜扫描和语音模式。在边缘人工智慧的背景下,在本地处理生物辨识资料可以减少延迟,提高隐私性,并确保即使在连接间歇性的情况下也能正常运作。行动资料包括行动装置产生的大量信息,例如位置资讯、应用程式使用统计数据和用户行为洞察。利用边缘AI处理行动资料可以显着增强服务个人化和即时决策能力。感测器资料是指嵌入设备和环境中的实体感测器的输出,捕捉温度、湿度、振动和运动等各种指标。边缘人工智慧会立即处理这些资料,以实现高效的营运回应。语音辨识技术使设备能够理解和处理人类语音命令,并将其转换为可操作的资料。与边缘人工智慧的整合有助于无缝互动并减少对云端处理的依赖。影片和影像识别分析视觉内容以识别物件、脸部、场景和活动。边缘人工智慧透过直接在相机和智慧型手机等设备上处理内容,支援监控、零售分析和自动驾驶等应用,加速了这项任务。
最终用途:政府和公共部门更多地采用边缘人工智慧,并专注于服务交付和资料安全
汽车产业越来越多地将边缘人工智慧解决方案应用于各种应用,包括自动驾驶、预测性维护和改善用户体验。边缘人工智慧使汽车能够透过本地处理资料来快速响应不断变化的环境,消除可能危及乘客安全的延迟。能源和公共产业正在采用边缘人工智慧来管理电网运作、优化能源流并提供基础设施的预测性维护。对能源发行网路的营运效率和进阶监控的需求至关重要,边缘人工智慧可以帮助公共产业公司根据瞬时资料做出即时决策。在政府和公共部门,边缘人工智慧正被用于智慧城市计画、公共和交通系统。此细分市场的需求是改善服务交付,同时确保公民隐私和安全。医疗领域透过增强的病患监测、医学影像分析和医院物流受益于边缘人工智慧。对边缘人工智慧的需求源于在局部快速处理大量敏感医疗资料以进行及时决策的紧迫性。製造业中的边缘人工智慧旨在品管、预测性维护和供应链优化。对这项技术的需求尤其受到工厂车间产生的大量资料点的驱动,这些数据点需要即时分析以提高生产率和安全性。通讯业者正在使用边缘人工智慧来优化网路、改善客户体验和预测分析。
应用:将边缘AI引入智慧穿戴设备,可以实现更准确的资料分析和更快的处理。
存取管理中的边缘人工智慧包括生物识别、安全系统和智慧锁技术。加强私营和公共部门安全通讯协定的需求正在推动人们对这些解决方案的偏好。边缘人工智慧可实现即时资料处理,减少延迟并提高决策速度。自动驾驶汽车(AV)中的边缘人工智慧是指使用本地处理的人工智慧演算法来即时执行路径规划、物件侦测和决策等任务。由于研究和开发的增加以及对更安全道路的推动,自动驾驶汽车中的边缘人工智慧变得越来越受欢迎。边缘人工智慧驱动的能源管理涉及优化能源使用并降低商业和工业环境中的营运成本。这种偏好源自于对永续和节能营运的追求。由边缘人工智慧驱动的精密农业可实现作物监测和土壤状况分析等智慧农业技术。对粮食安全和永续农业实践日益增长的需求正在提高其偏好。由边缘人工智慧支援的智慧型穿戴装置包括健身追踪器和医疗监测设备,可即时洞察个人健康指标。消费者对个人化健康资料和便利性的需求正在推动这些设备的扩展和偏好。遥测中的边缘人工智慧从航太和汽车等领域的远端或无法存取的位置收集和处理资料。对边缘人工智慧遥测的偏好是由资料传输中即时资料处理的需求所驱动的。具有边缘人工智慧的视讯监控用于零售和公共等领域的即时威胁检测和分析。边缘人工智慧在监控系统中受到青睐,因为它可以有效减少误报并提供即时分析。
区域洞察
由于云端基础技术的稳定采用和物联网设备的日益普及,美洲的边缘人工智慧市场正经历强劲成长。北美尤其是技术创新中心,老字型大小企业不断扩大其边缘人工智慧解决方案的产品范围。在欧洲、中东和非洲,边缘人工智慧市场充满活力且多元。在欧洲,《一般资料保护规范》(GDPR) 等严格的隐私法规正在推动向本地资料处理的转变,并刺激边缘人工智慧技术的发展。在中东,人工智慧在边缘的使用正在取得进展,以加强智慧城市计画以及石油和天然气业务。同时,非洲的投资正在成长,特别是在农业技术和医疗保健等领域,边缘人工智慧可以显着提高效率和可近性。由于中国、韩国和日本人工智慧技术渗透率的提高以及政府的支持,亚太地区显示出巨大的潜力,预计将在边缘人工智慧市场中呈现最高的成长率。亚太地区的大型製造地越来越多地采用人工智慧边缘运算来实现即时流程优化。此外,该地区蓬勃发展的消费性电子产业为将边缘人工智慧融入消费性设备提供了肥沃的土壤。
FPNV定位矩阵
FPNV定位矩阵对于评估边缘人工智慧市场至关重要。我们检视与业务策略和产品满意度相关的关键指标,以对供应商进行全面评估。这种深入的分析使用户能够根据自己的要求做出明智的决策。根据评估,供应商被分为四个成功程度不同的像限。最前线 (F)、探路者 (P)、利基 (N) 和重要 (V)。
市场占有率分析
市场占有率分析是一个综合工具,可以对边缘人工智慧市场供应商的现状进行深入而深入的研究。全面比较和分析供应商在整体收益、基本客群和其他关键指标方面的贡献,以便更好地了解公司的绩效及其在争夺市场占有率时面临的挑战。此外,该分析还提供了对该细分市场竞争特征的宝贵见解,包括在研究基准年观察到的累积、碎片化主导地位和合併特征等因素。详细程度的提高使供应商能够做出更明智的决策并制定有效的策略,从而在市场上获得竞争优势。
1. 市场渗透率:提供有关主要企业所服务的市场的全面资讯。
2. 市场开拓:我们深入研究利润丰厚的新兴市场,并分析其在成熟细分市场的渗透率。
3. 市场多元化:包括新产品发布、开拓地区、最新发展和投资的详细资讯。
4. 竞争评估和情报:对主要企业的市场占有率、策略、产品、认证、监管状况、专利状况和製造能力进行全面评估。
5. 产品开发与创新:包括对未来技术、研发活动和突破性产品开发的智力见解。
1.边缘人工智慧市场的市场规模与预测是多少?
2. 在边缘人工智慧市场预测期内,我们应该考虑投资哪些产品和应用?
3.边缘人工智慧市场的技术趋势和法规结构是什么?
4.边缘人工智慧市场主要厂商的市场占有率为何?
5. 进入边缘人工智慧市场的合适形式和策略手段是什么?
[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?