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市场调查报告书
商品编码
1530686

2030 年边缘 AI 硬体市场预测：按处理器类型、设备类型、部署、应用程式和区域进行的全球分析

Edge AI Hardware Market Forecasts to 2030 - Global Analysis By Processor Type (CPUs, GPUs, DSPs, NPUs, ASICs, FPGAs and Other Processor Types), Device Type, Deployment, Application and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的数据，2024 年全球边缘人工智慧硬体市场规模将达到 256.1 亿美元，预计到 2030 年将达到 558.2 亿美元，预测期内复合年增长率为 18.7%。

边缘人工智慧硬体是指设计用于在资料来源（边缘）附近或本地执行人工智慧（AI）任务的专用运算设备，而不是依赖集中式云端伺服器。边缘人工智慧硬体可以即时处理来自感测器和其他来源的资料，而无需持续的互联网连接，这使其成为速度、隐私和频宽限制至关重要的应用程式的理想选择。

根据 CNN Business报导，韩国政府将在 2027 年之前在人工智慧领域投资 69.4 亿美元，以保持其在尖端半导体晶片领域的世界领先地位。

即时分析的需求不断增长

边缘人工智慧硬体使设备能够在本地执行复杂的运算，从而减少延迟并更快地回应资料洞察。自动驾驶汽车、製造和医疗保健等行业需要即时分析以提高业务效率和安全性。透过部署边缘人工智慧硬件，企业可以更快地获得洞察、提高营运敏捷性并增强回应能力，以满足关键应用程式中对即时分析不断增长的需求。

可扩展性问题

边缘人工智慧硬体的可扩展性问题源自于跨不同环境部署和管理分散式系统的复杂性。挑战包括整合不同的设备、确保无缝互通性以及远端管理更新和维护。此外，扩展边缘人工智慧解决方案以满足不断增长的资料量和不断变化的应用程式需求需要强大的基础设施和经验丰富的专业知识。这些因素增加了实施成本和复杂性，限制了可扩展性，并阻碍了采用。

物联网设备快速增加

边缘人工智慧硬体对于本地处理此类资料、减少延迟和频宽要求、同时增强即时决策能力至关重要。此功能对于智慧城市、工业自动化和医疗保健等需要快速资料分析以提高业务效率和回应能力的应用至关重要。随着物联网应用的不断扩大，对边缘人工智慧硬体提供的高效分散式处理解决方案的需求预计将大幅增加。

整合的复杂性

整合边缘人工智慧硬体的复杂性源自于不同的硬体平台、软体框架以及与现有IT基础设施基础设施的兼容性问题。这种复杂性会增加​​实施成本、需要专门的技术知识，并可能增加解决方案的上市时间，从而阻碍市场成长。标准化通讯协定和互通性标准的缺乏进一步使整合工作变得复杂，并限制了不同边缘运算环境之间的可扩展性和互通性。

COVID-19 的影响

COVID-19 大流行凸显了远端工作设定、医疗保健监控和非接触式业务中分散式资料处理的需求，从而加速了边缘 AI 硬体的采用。该组织寻求一种能够保证即时资料分析并最大限度地减少对集中式基础设施的依赖的解决方案。这种转变推动了对边缘人工智慧硬体的需求增加，特别是在全球混乱时期优先考虑安全、效率和连续性的产业。

预计伺服器细分市场在预测期内将是最大的

伺服器领域预计将出现良好的成长。边缘人工智慧硬体中的边缘伺服器是指位于网路外围、靠近资料来源的专用运算设备。边缘伺服器促进人工智慧演算法的本地处理，透过处理更接近资料来源的资料来减少延迟和频宽消耗。边缘伺服器对于需要即时分析的应用程式（例如物联网部署和自治系统）至关重要，可以加快决策速度并提高整体系统效率和回应能力。

预计智慧城市领域在预测期内复合年增长率最高

预计智慧城市产业在预测期内将以最高的复合年增长率成长。边缘人工智慧硬体透过在网路边缘实现即时资料处理和决策，在智慧城市中发挥关键作用。这些设备有助于城市基础设施的高效管理。透过在本地处理资料，边缘人工智慧硬体可减少延迟、改善资源分配、增强公共并优化服务交付，以帮助推进和永续性。

比最大的地区

由于物联网设备的激增、5G 基础设施的进步以及製造、医疗保健和汽车等行业越来越多地采用人工智慧驱动的应用程序，预计亚太地区将在预测期内占据最大的市场占有率。中国、日本和韩国等国家在边缘人工智慧解决方案的创新和部署方面处于领先地位。该地区充满活力的产业格局和政府推动数位转型的倡议将进一步支持市场扩张。

复合年增长率最高的地区：

由于物联网、自主系统和智慧製造等技术进步，预计北美在预测期内将呈现最高的复合年增长率。推动市场扩张的关键因素包括对智慧城市计划的投资增加、对自动驾驶汽车的需求不断增长以及工业自动化和医疗保健领域连网型设备的激增。北美仍然是推动边缘人工智慧硬体技术进步和采用的关键地区。

免费客製化服务：

订阅此报告的客户可以存取以下免费自订选项之一：

  • 公司简介
    • 其他市场参与者的综合分析（最多 3 家公司）
    • 主要企业SWOT分析（最多3家企业）
  • 区域分割
    • 根据客户兴趣对主要国家的市场估计、预测和复合年增长率（註：基于可行性检查）
  • 竞争基准化分析
    • 根据产品系列、地理分布和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 前言

  • 概述
  • 相关利益者
  • 调查范围
  • 调查方法
    • 资料探勘
    • 资料分析
    • 资料检验
    • 研究途径
  • 研究资讯来源
    • 主要研究资讯来源
    • 二次研究资讯来源
    • 先决条件

第三章市场趋势分析

  • 促进因素
  • 抑制因素
  • 机会
  • 威胁
  • 应用分析
  • 新兴市场
  • COVID-19 的影响

第4章波特五力分析

  • 供应商的议价能力
  • 买方议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争公司之间的敌对关係

第五章全球边缘人工智慧硬体市场：按处理器类型

  • CPU（中央处理单元）
  • GPU（图形处理单元）
  • DSP（数位讯号处理器）
  • NPU（神经处理单元）
  • ASIC（专用积体电路）
  • FPGA（现场可程式化闸阵列）
  • 其他处理器类型

第六章全球边缘人工智慧硬体市场：按设备类型

  • 伺服器
  • 闸道
  • 计算设备
  • 相机
  • 机器人
  • 无人机
  • 其他设备类型

第七章全球边缘人工智慧硬体市场：按部署划分

  • 本地
  • 云边
  • 雾计算

第八章全球边缘人工智慧硬体市场：按应用分类

  • 自动驾驶汽车
  • 卫生保健
  • 监控和安全
  • 家电
  • 零售
  • 智慧城市
  • 其他用途

第九章全球边缘人工智慧硬体市场：按地区

  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲国家
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东/非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲

第10章 主要进展

  • 合约、伙伴关係、协作和合资企业
  • 收购和合併
  • 新产品发布
  • 业务拓展
  • 其他关键策略

第十一章 公司概况

  • NVIDIA
  • Intel
  • Qualcomm
  • Google
  • Synopsys
  • CEVA Inc.
  • Xilinx
  • Huawei
  • Samsung Electronics
  • NXP Semiconductors
  • Texas Instruments
  • Apple
  • Micron Technology
Product Code: SMRC26798

According to Stratistics MRC, the Global Edge AI Hardware Market is accounted for $25.61 billion in 2024 and is expected to reach $55.82 billion by 2030 growing at a CAGR of 18.7% during the forecast period. Edge AI hardware refers to specialized computing devices designed to perform artificial intelligence (AI) tasks locally, at or near the data source (the edge) rather than relying on centralized cloud servers. Edge AI hardware enables real-time processing of data from sensors and other sources without requiring constant internet connectivity, making it ideal for applications where speed, privacy, or bandwidth constraints are critical.

According to an article by CNN Business, the South Korean government will invest USD 6.94 billion in artificial intelligence by 2027 as part of efforts to retain a leading global position in cutting-edge semiconductor chips.

Market Dynamics:

Driver:

Increasing demand for real-time analytics

Edge AI hardware enables devices to perform complex computations locally, reducing latency and enabling quicker responses to data insights. Industries such as autonomous vehicles, manufacturing, and healthcare require instantaneous analytics for operational efficiency and safety. By deploying Edge AI hardware, organizations can achieve faster insights, improved operational agility, and enhanced responsiveness, thereby meeting the growing demand for real-time analytics in critical applications.

Restraint:

Scalability issues

Scalability issues in Edge AI hardware arise from complexities in deploying and managing distributed systems across diverse environments. Challenges include integrating heterogeneous devices, ensuring seamless interoperability, and managing updates and maintenance remotely. Furthermore, scaling edge AI solutions to accommodate growing data volumes and evolving application requirements requires robust infrastructure and skilled expertise. These factors increase deployment costs and complexity, limiting scalability and hindering widespread adoption.

Opportunity:

Proliferation of IoT devices

Edge AI hardware is essential for processing this data locally; reducing latency and bandwidth requirements while enhancing real-time decision-making capabilities. This capability is crucial in applications such as smart cities, industrial automation, and healthcare, where rapid data analysis is necessary for operational efficiency and responsiveness. As IoT deployments continue to expand, the demand for efficient, decentralized processing solutions provided by edge AI hardware is expected to rise significantly.

Threat:

Complexity in integration

Complexity in integrating Edge AI hardware arises due to diverse hardware platforms, software frameworks, and compatibility issues with existing IT infrastructures. This complexity hampers market growth by increasing deployment costs, requiring specialized technical expertise, and potentially extending time-to-market for solutions. Lack of standardized protocols and interoperability standards further complicates integration efforts, limiting scalability and interoperability across different edge computing environments.

Covid-19 Impact

The covid-19 pandemic accelerated the adoption of edge AI hardware by highlighting the need for decentralized data processing in remote work setups, healthcare monitoring, and contactless operations. Organizations sought solutions that could ensure real-time data analysis and minimize dependence on centralized infrastructure. This shift drove increased demand for edge AI hardware, particularly in sectors prioritizing safety, efficiency, and continuity during global disruptions.

The servers segment is expected to be the largest during the forecast period

The servers segment is estimated to have a lucrative growth. Edge servers in Edge AI hardware refer to specialized computing devices positioned at the periphery of networks, closer to data sources. They facilitate local processing of AI algorithms, reducing latency and bandwidth consumption by handling data closer to its origin. Edge servers are crucial for applications requiring real-time analytics, such as IoT deployments and autonomous systems, enabling faster decision-making and enhancing overall system efficiency and responsiveness.

The smart cities segment is expected to have the highest CAGR during the forecast period

The smart cities segment is anticipated to witness the highest CAGR growth during the forecast period. Edge AI hardware plays a crucial role in smart cities by enabling real-time data processing and decision-making at the edge of the network. These devices facilitate efficient management of urban infrastructure. By processing data locally, Edge AI hardware reduces latency, improves resource allocation, enhances public safety, and optimizes service delivery, thereby supporting the development and sustainability of smart city initiatives.

Region with largest share:

Asia Pacific is projected to hold the largest market share during the forecast period driven by the proliferation of IoT devices, advancements in 5G infrastructure, and increasing adoption of AI-driven applications across industries such as manufacturing, healthcare, and automotive. Countries like China, Japan, and South Korea are leading in technological innovation and deployment of edge AI solutions. The region's dynamic industrial landscape and government initiatives promoting digital transformation further bolster market expansion.

Region with highest CAGR:

North America is projected to have the highest CAGR over the forecast period driven by the region's technological advancements, particularly in IoT, autonomous systems, and smart manufacturing. Key factors propelling market expansion include increasing investments in smart city initiatives, rising demand for autonomous vehicles, and the proliferation of connected devices in industrial automation and healthcare sectors. North America remains a pivotal region for driving advancements and adoption of Edge AI hardware technologies.

Key players in the market

Some of the key players profiled in the Edge AI Hardware Market include NVIDIA, Intel, Qualcomm, Google, Synopsys, CEVA Inc., Xilinx, Huawei, Samsung Electronics, NXP Semiconductors, Texas Instruments, Apple and Micron Technology.

Key Developments:

In July 2024, Google launched distributed cloud edge hardware to run AI workloads in or outside its data centers. The Google Distributed Cloud (GDC) air-gapped appliance is mostly for highly regulated organizations that must keep data in-house. The hardware runs the Google Cloud infrastructure stack, data security services and Vertex AI platform. Vertex AI runs models that have been pretrained for various tasks.

In September 2022, NVIDIA introduced the NVIDIA IGX platform for high-precision edge AI, bringing advanced security and proactive safety to sensitive industries such as manufacturing, logistics and healthcare. NVIDIA IGX will help companies build the next generation of software-defined industrial and medical devices that can safely operate in the same environment as humans.

Processor Types Covered:

  • CPUs (Central Processing Units)
  • GPUs (Graphics Processing Units)
  • DSPs (Digital Signal Processors)
  • NPUs (Neural Processing Units)
  • ASICs (Application-Specific Integrated Circuits)
  • FPGAs (Field-Programmable Gate Arrays)
  • Other Processor Types

Device Types Covered:

  • Servers
  • Gateways
  • Computing Devices
  • Cameras
  • Robots
  • Drones
  • Other Device Types

Deployments Covered:

  • On-Premises
  • Cloud-Edge
  • Fog Computing

Applications Covered:

  • Autonomous Vehicles
  • Healthcare
  • Surveillance & Security
  • Consumer Electronics
  • Retail
  • Smart Cities
  • Other Applications

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2022, 2023, 2024, 2026, and 2030
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Application Analysis
  • 3.7 Emerging Markets
  • 3.8 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global Edge AI Hardware Market, By Processor Type

  • 5.1 Introduction
  • 5.2 CPUs (Central Processing Units)
  • 5.3 GPUs (Graphics Processing Units)
  • 5.4 DSPs (Digital Signal Processors)
  • 5.5 NPUs (Neural Processing Units)
  • 5.6 ASICs (Application-Specific Integrated Circuits)
  • 5.7 FPGAs (Field-Programmable Gate Arrays)
  • 5.8 Other Processor Types

6 Global Edge AI Hardware Market, By Device Type

  • 6.1 Introduction
  • 6.2 Servers
  • 6.3 Gateways
  • 6.4 Computing Devices
  • 6.5 Cameras
  • 6.6 Robots
  • 6.7 Drones
  • 6.8 Other Device Types

7 Global Edge AI Hardware Market, By Deployment

  • 7.1 Introduction
  • 7.2 On-Premises
  • 7.3 Cloud-Edge
  • 7.4 Fog Computing

8 Global Edge AI Hardware Market, By Application

  • 8.1 Introduction
  • 8.2 Autonomous Vehicles
  • 8.3 Healthcare
  • 8.4 Surveillance & Security
  • 8.5 Consumer Electronics
  • 8.6 Retail
  • 8.7 Smart Cities
  • 8.8 Other Applications

9 Global Edge AI Hardware Market, By Geography

  • 9.1 Introduction
  • 9.2 North America
    • 9.2.1 US
    • 9.2.2 Canada
    • 9.2.3 Mexico
  • 9.3 Europe
    • 9.3.1 Germany
    • 9.3.2 UK
    • 9.3.3 Italy
    • 9.3.4 France
    • 9.3.5 Spain
    • 9.3.6 Rest of Europe
  • 9.4 Asia Pacific
    • 9.4.1 Japan
    • 9.4.2 China
    • 9.4.3 India
    • 9.4.4 Australia
    • 9.4.5 New Zealand
    • 9.4.6 South Korea
    • 9.4.7 Rest of Asia Pacific
  • 9.5 South America
    • 9.5.1 Argentina
    • 9.5.2 Brazil
    • 9.5.3 Chile
    • 9.5.4 Rest of South America
  • 9.6 Middle East & Africa
    • 9.6.1 Saudi Arabia
    • 9.6.2 UAE
    • 9.6.3 Qatar
    • 9.6.4 South Africa
    • 9.6.5 Rest of Middle East & Africa

10 Key Developments

  • 10.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 10.2 Acquisitions & Mergers
  • 10.3 New Product Launch
  • 10.4 Expansions
  • 10.5 Other Key Strategies

11 Company Profiling

  • 11.1 NVIDIA
  • 11.2 Intel
  • 11.3 Qualcomm
  • 11.4 Google
  • 11.5 Synopsys
  • 11.6 CEVA Inc.
  • 11.7 Xilinx
  • 11.8 Huawei
  • 11.9 Samsung Electronics
  • 11.10 NXP Semiconductors
  • 11.11 Texas Instruments
  • 11.12 Apple
  • 11.13 Micron Technology

List of Tables

  • Table 1 Global Edge AI Hardware Market Outlook, By Region (2022-2030) ($MN)
  • Table 2 Global Edge AI Hardware Market Outlook, By Processor Type (2022-2030) ($MN)
  • Table 3 Global Edge AI Hardware Market Outlook, By CPUs (Central Processing Units) (2022-2030) ($MN)
  • Table 4 Global Edge AI Hardware Market Outlook, By GPUs (Graphics Processing Units) (2022-2030) ($MN)
  • Table 5 Global Edge AI Hardware Market Outlook, By DSPs (Digital Signal Processors) (2022-2030) ($MN)
  • Table 6 Global Edge AI Hardware Market Outlook, By NPUs (Neural Processing Units) (2022-2030) ($MN)
  • Table 7 Global Edge AI Hardware Market Outlook, By ASICs (Application-Specific Integrated Circuits) (2022-2030) ($MN)
  • Table 8 Global Edge AI Hardware Market Outlook, By FPGAs (Field-Programmable Gate Arrays) (2022-2030) ($MN)
  • Table 9 Global Edge AI Hardware Market Outlook, By Other Processor Types (2022-2030) ($MN)
  • Table 10 Global Edge AI Hardware Market Outlook, By Device Type (2022-2030) ($MN)
  • Table 11 Global Edge AI Hardware Market Outlook, By Servers (2022-2030) ($MN)
  • Table 12 Global Edge AI Hardware Market Outlook, By Gateways (2022-2030) ($MN)
  • Table 13 Global Edge AI Hardware Market Outlook, By Computing Devices (2022-2030) ($MN)
  • Table 14 Global Edge AI Hardware Market Outlook, By Cameras (2022-2030) ($MN)
  • Table 15 Global Edge AI Hardware Market Outlook, By Robots (2022-2030) ($MN)
  • Table 16 Global Edge AI Hardware Market Outlook, By Drones (2022-2030) ($MN)
  • Table 17 Global Edge AI Hardware Market Outlook, By Other Device Types (2022-2030) ($MN)
  • Table 18 Global Edge AI Hardware Market Outlook, By Deployment (2022-2030) ($MN)
  • Table 19 Global Edge AI Hardware Market Outlook, By On-Premises (2022-2030) ($MN)
  • Table 20 Global Edge AI Hardware Market Outlook, By Cloud-Edge (2022-2030) ($MN)
  • Table 21 Global Edge AI Hardware Market Outlook, By Fog Computing (2022-2030) ($MN)
  • Table 22 Global Edge AI Hardware Market Outlook, By Application (2022-2030) ($MN)
  • Table 23 Global Edge AI Hardware Market Outlook, By Autonomous Vehicles (2022-2030) ($MN)
  • Table 24 Global Edge AI Hardware Market Outlook, By Healthcare (2022-2030) ($MN)
  • Table 25 Global Edge AI Hardware Market Outlook, By Surveillance & Security (2022-2030) ($MN)
  • Table 26 Global Edge AI Hardware Market Outlook, By Consumer Electronics (2022-2030) ($MN)
  • Table 27 Global Edge AI Hardware Market Outlook, By Retail (2022-2030) ($MN)
  • Table 28 Global Edge AI Hardware Market Outlook, By Smart Cities (2022-2030) ($MN)
  • Table 29 Global Edge AI Hardware Market Outlook, By Other Applications (2022-2030) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.