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1530686

2030 年邊緣 AI 硬體市場預測：按處理器類型、設備類型、部署、應用程式和區域進行的全球分析

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個工作天內

價格

根據 Stratistics MRC 的數據，2024 年全球邊緣人工智慧硬體市場規模將達到 256.1 億美元，預計到 2030 年將達到 558.2 億美元，預測期內複合年成長率為 18.7%。

邊緣人工智慧硬體是指設計用於在資料來源（邊緣）附近或本地執行人工智慧（AI）任務的專用運算設備，而不是依賴集中式雲端伺服器。邊緣人工智慧硬體可以即時處理來自感測器和其他來源的資料，而無需持續的網際網路連接，這使其成為速度、隱私和頻寬限制至關重要的應用程式的理想選擇。

根據 CNN Business報導，韓國政府將在 2027 年之前在人工智慧領域投資 69.4 億美元，以保持其在尖端半導體晶片領域的世界領先地位。

即時分析的需求不斷成長

邊緣人工智慧硬體使設備能夠在本地執行複雜的運算，從而減少延遲並更快地回應資料洞察。自動駕駛汽車、製造和醫療保健等行業需要即時分析以提高業務效率和安全性。透過部署邊緣人工智慧硬體，企業可以更快地獲得洞察、提高營運敏捷性並增強回應能力，以滿足關鍵應用程式中對即時分析不斷成長的需求。

可擴展性問題

邊緣人工智慧硬體的可擴展性問題源自於跨不同環境部署和管理分散式系統的複雜性。挑戰包括整合不同的設備、確保無縫互通性以及遠端管理更新和維護。此外，擴展邊緣人工智慧解決方案以滿足不斷成長的資料量和不斷變化的應用程式需求需要強大的基礎設施和經驗豐富的專業知識。這些因素增加了實施成本和複雜性，限制了可擴展性，並阻礙了採用。

物聯網設備快速增加

邊緣人工智慧硬體對於本地處理此類資料、減少延遲和頻寬要求、同時增強即時決策能力至關重要。此功能對於智慧城市、工業自動化和醫療保健等需要快速資料分析以提高業務效率和回應能力的應用至關重要。隨著物聯網應用的不斷擴大，對邊緣人工智慧硬體提供的高效分散式處理解決方案的需求預計將大幅增加。

整合的複雜性

整合邊緣人工智慧硬體的複雜性源自於不同的硬體平台、軟體框架以及與現有IT基礎設施基礎設施的兼容性問題。這種複雜性會增加​​實施成本、需要專門的技術知識，並可能增加解決方案的上市時間，從而阻礙市場成長。標準化通訊協定和互通性標準的缺乏進一步使整合工作變得複雜，並限制了不同邊緣運算環境之間的可擴展性和互通性。

COVID-19 的影響

COVID-19 大流行凸顯了遠端工作設定、醫療保健監控和非接觸式業務中分散式資料處理的需求，從而加速了邊緣 AI 硬體的採用。該組織尋求一種能夠保證即時資料分析並最大限度地減少對集中式基礎設施的依賴的解決方案。這種轉變推動了對邊緣人工智慧硬體的需求增加，特別是在全球混亂時期優先考慮安全、效率和連續性的產業。

預計伺服器細分市場在預測期內將是最大的

伺服器領域預計將出現良好的成長。邊緣人工智慧硬體中的邊緣伺服器是指位於網路外圍、靠近資料來源的專用運算設備。邊緣伺服器促進人工智慧演算法的本地處理，透過處理更接近資料來源的資料來減少延遲和頻寬消耗。邊緣伺服器對於需要即時分析的應用程式（例如物聯網部署和自治系統）至關重要，可以加快決策速度並提高整體系統效率和回應能力。

預計智慧城市領域在預測期內複合年成長率最高

預計智慧城市產業在預測期內將以最高的複合年成長率成長。邊緣人工智慧硬體透過在網路邊緣實現即時資料處理和決策，在智慧城市中發揮關鍵作用。這些設備有助於城市基礎設施的高效管理。透過在本地處理資料，邊緣人工智慧硬體可減少延遲、改善資源分配、增強公共並最佳化服務交付，以幫助推進和永續性。

比最大的地區

由於物聯網設備的激增、5G 基礎設施的進步以及製造、醫療保健和汽車等行業擴大採用人工智慧驅動的應用程式，預計亞太地區將在預測期內佔據最大的市場佔有率。中國、日本和韓國等國家在邊緣人工智慧解決方案的創新和部署方面處於領先地位。該地區充滿活力的產業格局和政府推動數位轉型的舉措將進一步支持市場擴張。

複合年成長率最高的地區：

由於物聯網、自主系統和智慧製造等技術進步，預計北美在預測期內將呈現最高的複合年成長率。推動市場擴張的關鍵因素包括對智慧城市計劃的投資增加、對自動駕駛汽車的需求不斷成長以及工業自動化和醫療保健領域連網型設備的激增。北美仍然是推動邊緣人工智慧硬體技術進步和採用的關鍵地區。

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目錄

第1章執行摘要

第2章 前言

  • 概述
  • 相關利益者
  • 調查範圍
  • 調查方法
    • 資料探勘
    • 資料分析
    • 資料檢驗
    • 研究途徑
  • 研究資訊來源
    • 主要研究資訊來源
    • 二次研究資訊來源
    • 先決條件

第3章市場趨勢分析

  • 促進因素
  • 抑制因素
  • 機會
  • 威脅
  • 應用分析
  • 新興市場
  • COVID-19 的影響

第4章波特五力分析

  • 供應商的議價能力
  • 買方議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭公司之間的敵對關係

第5章全球邊緣人工智慧硬體市場：按處理器類型

  • CPU（中央處理單元）
  • GPU（圖形處理單元）
  • DSP（數位訊號處理器）
  • NPU（神經處理單元）
  • ASIC（專用積體電路）
  • FPGA（現場可程式化閘陣列）
  • 其他處理器類型

第6章全球邊緣人工智慧硬體市場：按設備類型

  • 伺服器
  • 閘道
  • 計算設備
  • 相機
  • 機器人
  • 無人機
  • 其他設備類型

第7章全球邊緣人工智慧硬體市場：按部署分類

  • 本地
  • 雲邊
  • 霧計算

第8章全球邊緣人工智慧硬體市場：按應用分類

  • 自動駕駛汽車
  • 衛生保健
  • 監控和安全
  • 家電
  • 零售
  • 智慧城市
  • 其他用途

第9章全球邊緣人工智慧硬體市場：按地區

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲國家
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 其他亞太地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地區
  • 中東/非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲

第10章 主要進展

  • 合約、夥伴關係、協作和合資企業
  • 收購和合併
  • 新產品發布
  • 業務拓展
  • 其他關鍵策略

第11章 公司概況

  • 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.