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邊緣人工智慧處理器市場預測至2032年：按處理器類型、記憶體架構、連接介面、部署模式、應用、最終用戶和地區分類的全球分析

Edge AI Processors Market Forecasts to 2032 - Global Analysis By Processor Type, Memory Architecture, Connectivity Interface, Deployment Mode, Application, End User, and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的一項研究，預計到 2025 年，全球邊緣 AI 處理器市場價值將達到 43 億美元，到 2032 年將達到 78 億美元，預測期內複合年成長率為 8.8%。

邊緣人工智慧處理器是先進的半導體晶片，旨在直接在本地設備上執行人工智慧任務，從而無需依賴遠端雲端伺服器。它們整合了加速器和最佳化的記憶體層次結構，能夠為自動駕駛、工業IoT、機器人和智慧監控等關鍵應用提供高效能運算，實現即時決策。透過最大限度地降低延遲、減少頻寬使用並增強資料隱私，這些處理器能夠實現更快、更安全、更有效率的運行，使其成為下一代智慧互聯系統的重要組成部分。

自主系統和物聯網的發展

自主系統和物聯網設備的快速擴張正推動著對邊緣人工智慧處理器的強勁需求。這些晶片能夠直接在本地設備上進行即時決策，從而降低延遲並減少對雲端基礎設施的依賴。其應用涵蓋自動駕駛汽車、工業機器人、智慧監控、連線健診醫療以及其他對即時回應至關重要的領域。隨著全球數十億物聯網終端的激增，邊緣人工智慧處理器對於建立下一代互聯生態系統至關重要，它們能夠提供可擴展的智慧，並確保效率、安全性和響應速度。

分段式軟體和工具鏈支持

儘管硬體不斷進步，但軟體生態系統的割裂和工具鏈支援的不足仍然是邊緣人工智慧處理器發展的主要限制因素。開發者在最佳化跨架構工作負載方面面臨許多挑戰，導致效率低和推廣緩慢。缺乏標準化框架使得與現有系統的整合變得複雜，而專有解決方案則增加了成本並限制了互通性。這種割裂阻礙了可擴展性，抑制了中小企業的發展，並減緩了創新。如果沒有統一的平台和強大的開發者支持，邊緣人工智慧處理器將面臨無法充分利用、無法在關鍵即時應用中發揮其真正潛力的風險。

邊緣雲端混合編配平台

邊緣雲端混合編配平台為邊緣人工智慧處理器帶來了變革性的機會。透過結合本地推理和雲端分析，這些系統能夠提供最佳化的效能、可擴展性和柔軟性。企業可以在邊緣處理敏感數據，從而保障隱私和速度，同時利用雲端資源獲得更深入的洞察和模型訓練。這種混合方法支援從智慧城市到自動駕駛車隊等各種應用場景，並可在分散式環境中實現無縫協作。這使得邊緣人工智慧處理器成為未來智慧基礎設施的核心。

邊緣部署中的安全漏洞

邊緣環境的安全漏洞對邊緣人工智慧處理器市場構成重大威脅。分散式架構增加了遭受網路攻擊、資料外洩和惡意干擾的風險。與集中式雲端系統不同，邊緣設備通常缺乏強大的安全通訊協定，使其成為攻擊的理想目標。一旦處理器遭到入侵，可能會擾亂自動駕駛、工業IoT網路和醫療保健系統，造成嚴重後果。應對這些風險需要先進的加密技術、安全啟動機制和持續監控。如果沒有強而有力的保護措施，邊緣人工智慧的普及可能會停滯不前，人們對邊緣智慧的信任度也會下降。

新冠疫情的影響

新冠疫情加速了數位轉型和遠距辦公，推動了醫療保健、監控和工業自動化領域對邊緣人工智慧處理器的需求。部分地區雲端存取限制使得邊緣運算在即時、隱私敏感型任務中變得更加重要。然而，晶片短缺和生產中斷影響了供應，導致產品發布延遲。疫情凸顯了分散式智慧的重要性，推動了對用於自主系統、智慧城市和非接觸式技術的邊緣人工智慧的投資。該市場被視為後疫情時代韌性的關鍵基礎。

預計在預測期內，邊緣人工智慧專用積體電路 (ASIC) 細分市場將佔據最大的市場佔有率。

由於其架構專注於高效推理和低功耗，邊緣人工智慧專用積體電路（ASIC）預計將在預測期內佔據最大的市場佔有率。這些晶片針對特定的人工智慧工作負載提供最佳化的性能，從而支援重型電動車（​​EV）的即時決策。它們的整合支援高級駕駛輔助系統（ADAS）、預測性維護和自動駕駛功能。 ASIC 的可擴展性和成本效益使其成為尋求每瓦性能優勢的原始設備製造商（OEM）的理想選擇，推動了其在商用電動車平台上的應用。

預計在預測期內，LPDDR4/LPDDR5一體化記憶體細分市場將呈現最高的複合年成長率。

預計在預測期內，LPDDR4/LPDDR5整合記憶體市場將保持最高的成長率，這主要得益於其高頻寬和低功耗的完美平衡。這些記憶體類型對於電動車動力傳動系統中的即時感測器資料處理、人工智慧推理和多媒體處理至關重要。其緊湊的尺寸和優異的散熱性能使其非常適合在資源受限的邊緣環境中部署。隨著電動車向智慧互聯平台演進，對基於LPDDR的記憶體架構的需求預計將大幅成長，尤其是在需要快速啟動和低延遲的應用中。

比最大的地區

亞太地區預計將在整個預測期內保持最大的市場佔有率，這主要得益於中國、日本和韓國強力的政府支持政策、快速的都市化以及積極的電氣化目標。該地區擁有強大的製造業生態系統、成本效益高的勞動力以及大規模的電動車生產能力。對電池技術、充電基礎設施和人工智慧驅動的出行解決方案的策略性投資進一步鞏固了其優勢。亞太地區的整車製造商和一級供應商正在加速創新，使該地區成為重型電動車動力傳動系統領域的成長中心。

預計年複合成長率最高的地區

在預測期內，北美地區預計將實現最高的複合年成長率，這主要得益於嚴格的排放法規、車隊電氣化強制令以及對永續物流日益成長的需求。聯邦和州政府層級的獎勵正在推動商用車隊採用電動車，尤其是在最後一公里配送和遠距貨運領域。對人工智慧驅動的車輛智慧的重視，以及電池和溫度控管系統的進步，正在促進電動車的快速部署。汽車製造商、科技公司和公共產業公司之間的合作，為下一代電動車動力傳動系統系統的創新創造了沃土。

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目錄

第1章執行摘要

第2章 前言

  • 摘要
  • 相關利益者
  • 調查範圍
  • 調查方法
  • 研究材料

第3章 市場趨勢分析

  • 促進要素
  • 抑制因素
  • 機會
  • 威脅
  • 應用分析
  • 終端用戶分析
  • 新興市場
  • 新冠疫情的感染疾病

第4章 波特五力分析

  • 供應商的議價能力
  • 買方的議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

5. 全球邊緣人工智慧處理器市場（按處理器類型分類）

  • CPU
  • 邊緣 GPU
  • NPU/神經網路引擎
  • 視覺處理單元
  • 用於邊緣人工智慧的專用積體電路 (ASIC)
  • FPGA

6. 全球邊緣人工智慧處理器市場（按記憶體架構分類）

  • 片上SRAM
  • LPDDR4/LPDDR5 整合
  • HBM（高頻寬記憶體）
  • 統一記憶體存取模型

7. 全球邊緣人工智慧處理器市場（依連接介面分類）

  • PCIe
  • USB-C/Thunderbolt
  • 乙太網路/TSN
  • Wi-Fi 6/6E/7
  • 5G NR/毫米波

8. 全球邊緣人工智慧處理器市場依部署模式分類

  • 嵌入式裝置
  • 邊緣閘道器
  • 感測器和模組
  • 機器人系統
  • 工業邊緣節點

第9章 全球邊緣人工智慧處理器市場（按應用分類）

  • 智慧監控
  • 自主機器
  • 智慧型手機和穿戴式裝置
  • 智慧家庭設備
  • 工業自動化

第10章 全球邊緣人工智慧處理器市場（按最終用戶分類）

  • 家用電器
  • 汽車出行
  • 工業公司
  • 醫療保健

第11章 全球邊緣人工智慧處理器市場（按地區分類）

  • 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
  • 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
    • 其他南美國家
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第12章 重大進展

  • 協議、夥伴關係、合作和合資企業
  • 併購
  • 新產品發布
  • 業務拓展
  • 其他關鍵策略

第13章：企業概況

  • Qualcomm
  • NVIDIA
  • Apple
  • Intel
  • Samsung Electronics
  • Arm Ltd.
  • Google
  • MediaTek
  • Huawei
  • Ambarella
  • Graphcore
  • Baidu Kunlun
  • EdgeQ
  • Cadence Design Systems
  • Rockchip
Product Code: SMRC32832

According to Stratistics MRC, the Global Edge AI Processors Market is accounted for $4.3 billion in 2025 and is expected to reach $7.8 billion by 2032 growing at a CAGR of 8.8% during the forecast period. Edge AI processors are advanced semiconductor chips designed to execute artificial intelligence tasks directly on local devices, eliminating dependence on remote cloud servers. Equipped with integrated accelerators and optimized memory hierarchies, they deliver high-performance computing for real-time decision-making in critical applications such as autonomous driving, industrial IoT, robotics, and smart surveillance. By minimizing latency, reducing bandwidth usage, and enhancing data privacy, these processors enable faster, safer, and more efficient operations, making them indispensable components in next-generation intelligent and connected systems.

Market Dynamics:

Driver:

Growth in autonomous systems and IoT

The rapid expansion of autonomous systems and IoT devices is driving strong demand for edge AI processors. These chips enable real-time decision-making directly on local devices, reducing latency and dependence on cloud infrastructure. Applications span autonomous vehicles, industrial robotics, smart surveillance, and connected healthcare, where immediate responses are critical. As billions of IoT endpoints proliferate globally, edge AI processors provide scalable intelligence, ensuring efficiency, safety, and responsiveness, making them indispensable in next-generation connected ecosystems.

Restraint:

Fragmented software and toolchain support

Despite hardware advances, fragmented software ecosystems and limited toolchain support remain major restraints for edge AI processors. Developers face challenges in optimizing workloads across diverse architectures, leading to inefficiencies and slower adoption. Lack of standardized frameworks complicates integration with existing systems, while proprietary solutions increase costs and limit interoperability. This fragmentation hinders scalability, discourages smaller enterprises, and slows innovation. Without unified platforms and robust developer support, edge AI processors risk underutilization, delaying their full potential in critical real-time applications.

Opportunity:

Edge-cloud hybrid orchestration platforms

Edge-cloud hybrid orchestration platforms present a transformative opportunity for edge AI processors. By combining local inference with cloud-based analytics, these systems deliver optimized performance, scalability, and flexibility. Enterprises can process sensitive data at the edge for privacy and speed, while leveraging cloud resources for deeper insights and model training. This hybrid approach supports diverse use cases, from smart cities to autonomous fleets, enabling seamless coordination across distributed environments. It positions edge AI processors as central to future intelligent infrastructure.

Threat:

Security vulnerabilities in edge deployment

Security vulnerabilities in edge deployments pose a critical threat to the edge AI processor market. Distributed architectures increase exposure to cyberattacks, data breaches, and malicious interference. Unlike centralized cloud systems, edge devices often lack robust security protocols, making them attractive targets. Compromised processors can disrupt autonomous operations, industrial IoT networks, or healthcare systems, leading to severe consequences. Addressing these risks requires advanced encryption, secure boot mechanisms, and continuous monitoring. Without strong safeguards, adoption may stall, undermining trust in edge intelligence.

Covid-19 Impact:

COVID-19 accelerated digital transformation and remote operations, boosting demand for edge AI processors in healthcare, surveillance, and industrial automation. With cloud access constrained in some regions, edge computing gained prominence for real-time, privacy-sensitive tasks. However, chip shortages and manufacturing disruptions impacted availability and delayed product launches. The pandemic underscored the importance of decentralized intelligence, driving investment in edge AI for autonomous systems, smart cities, and contactless technologies, positioning the market as a critical enabler of post-COVID resilience.

The ASICs for edge AI segment is expected to be the largest during the forecast period

The ASICs for edge AI segment is expected to account for the largest market share during the forecast period, due to its tailored architecture for high-efficiency inference at low power. These chips offer optimized performance for specific AI workloads, enabling real-time decision-making in heavy-duty EVs. Their integration supports advanced driver-assistance systems (ADAS), predictive maintenance, and autonomous capabilities. The scalability and cost-effectiveness of ASICs make them ideal for OEMs seeking performance-per-watt advantages, driving widespread adoption across commercial EV platforms.

The LPDDR4/LPDDR5 integration segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the LPDDR4/LPDDR5 integration segment is predicted to witness the highest growth rate, driven by its balance of high bandwidth and low power consumption. These memory types are critical for handling real-time sensor data, AI inference, and multimedia processing in EV powertrains. Their compact form factor and thermal efficiency suit edge deployments in constrained environments. As EVs evolve toward intelligent, connected platforms, demand for LPDDR-based memory architectures will surge, especially in applications requiring fast boot times and low latency.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share, fueled by strong government incentives, rapid urbanization, and aggressive electrification targets in China, Japan, and South Korea. The region benefits from robust manufacturing ecosystems, cost-effective labor, and high-volume EV production. Strategic investments in battery technologies, charging infrastructure, and AI-enabled mobility solutions further reinforce its dominance. OEMs and Tier-1 suppliers in Asia Pacific are accelerating innovation, making it the epicenter of heavy-duty EV powertrain growth.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR, propelled by stringent emission regulations, fleet electrification mandates, and rising demand for sustainable logistics. Federal and state-level incentives are catalyzing adoption among commercial fleets, especially in last-mile delivery and long-haul trucking. The region's focus on AI-driven vehicle intelligence, coupled with advancements in battery and thermal management systems, supports rapid deployment. Collaborations between automakers, tech firms, and utilities are creating a fertile ground for next-gen EV powertrain innovation.

Key players in the market

Some of the key players in Heavy-Duty EV Powertrain Market include Qualcomm, NVIDIA, Apple, Intel, Samsung Electronics, Arm Ltd., Google, MediaTek, Huawei, Ambarella, Graphcore, Baidu Kunlun, EdgeQ, Cadence Design Systems, and Rockchip.

Key Developments:

In June 2025, Apple officially exited its Project Titan EV program, ending ambitions for an Apple Car, while competitors in China accelerated EV powertrain innovation, reshaping competitive dynamics in the sector.

In March 2025, NVIDIA collaborated with SES AI to accelerate discovery of novel EV battery materials using GPU-accelerated simulations and domain-adapted LLMs, enhancing energy density and performance for heavy-duty EV powertrains.

In January 2025, Qualcomm partnered with Mahindra to power its first Electric Origin SUV range using Snapdragon Digital Chassis solutions, enabling AI-driven safety features, 5G connectivity, and advanced cockpit compute for heavy-duty EV applications.

Processor Types Covered:

  • CPUs
  • Edge GPUs
  • NPUs / Neural Engines
  • Vision Processing Units
  • ASICs for Edge AI
  • FPGAs

Memory Architectures Covered:

  • Sensors
  • Probes and Analyzers
  • Software and Services

Connectivity Interfaces Covered:

  • PCIe
  • USB-C / Thunderbolt
  • Ethernet / TSN
  • Wi-Fi 6 / 6E / 7
  • 5G NR / mmWave

Deployment Modes Covered:

  • Embedded Devices
  • Edge Gateways
  • Sensors & Modules
  • Robotic Systems
  • Industrial Edge Nodes

Applications Covered:

  • Smart Surveillance
  • Autonomous Machines
  • Smartphones & Wearables
  • Smart Home Devices
  • Industrial Automation

End Users Covered:

  • Consumer Electronics
  • Automotive & Mobility
  • Industrial Enterprises
  • Healthcare

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 2024, 2025, 2026, 2028, and 2032
  • 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 End User Analysis
  • 3.8 Emerging Markets
  • 3.9 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 Processors Market, By Processor Type

  • 5.1 Introduction
  • 5.2 CPUs
  • 5.3 Edge GPUs
  • 5.4 NPUs / Neural Engines
  • 5.5 Vision Processing Units
  • 5.6 ASICs for Edge AI
  • 5.7 FPGAs

6 Global Edge AI Processors Market, By Memory Architecture

  • 6.1 Introduction
  • 6.2 On-Chip SRAM
  • 6.3 LPDDR4/LPDDR5 Integration
  • 6.4 HBM (High Bandwidth Memory)
  • 6.5 Unified Memory Access Models

7 Global Edge AI Processors Market, By Connectivity Interface

  • 7.1 Introduction
  • 7.2 PCIe
  • 7.3 USB-C / Thunderbolt
  • 7.4 Ethernet / TSN
  • 7.5 Wi-Fi 6 / 6E / 7
  • 7.6 5G NR / mmWave

8 Global Edge AI Processors Market, By Deployment Mode

  • 8.1 Introduction
  • 8.2 Embedded Devices
  • 8.3 Edge Gateways
  • 8.4 Sensors & Modules
  • 8.5 Robotic Systems
  • 8.6 Industrial Edge Nodes

9 Global Edge AI Processors Market, By Application

  • 9.1 Introduction
  • 9.2 Smart Surveillance
  • 9.3 Autonomous Machines
  • 9.4 Smartphones & Wearables
  • 9.5 Smart Home Devices
  • 9.6 Industrial Automation

10 Global Edge AI Processors Market, By End User

  • 10.1 Introduction
  • 10.2 Consumer Electronics
  • 10.3 Automotive & Mobility
  • 10.4 Industrial Enterprises
  • 10.5 Healthcare

11 Global Edge AI Processors Market, By Geography

  • 11.1 Introduction
  • 11.2 North America
    • 11.2.1 US
    • 11.2.2 Canada
    • 11.2.3 Mexico
  • 11.3 Europe
    • 11.3.1 Germany
    • 11.3.2 UK
    • 11.3.3 Italy
    • 11.3.4 France
    • 11.3.5 Spain
    • 11.3.6 Rest of Europe
  • 11.4 Asia Pacific
    • 11.4.1 Japan
    • 11.4.2 China
    • 11.4.3 India
    • 11.4.4 Australia
    • 11.4.5 New Zealand
    • 11.4.6 South Korea
    • 11.4.7 Rest of Asia Pacific
  • 11.5 South America
    • 11.5.1 Argentina
    • 11.5.2 Brazil
    • 11.5.3 Chile
    • 11.5.4 Rest of South America
  • 11.6 Middle East & Africa
    • 11.6.1 Saudi Arabia
    • 11.6.2 UAE
    • 11.6.3 Qatar
    • 11.6.4 South Africa
    • 11.6.5 Rest of Middle East & Africa

12 Key Developments

  • 12.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 12.2 Acquisitions & Mergers
  • 12.3 New Product Launch
  • 12.4 Expansions
  • 12.5 Other Key Strategies

13 Company Profiling

  • 13.1 Qualcomm
  • 13.2 NVIDIA
  • 13.3 Apple
  • 13.4 Intel
  • 13.5 Samsung Electronics
  • 13.6 Arm Ltd.
  • 13.7 Google
  • 13.8 MediaTek
  • 13.9 Huawei
  • 13.10 Ambarella
  • 13.11 Graphcore
  • 13.12 Baidu Kunlun
  • 13.13 EdgeQ
  • 13.14 Cadence Design Systems
  • 13.15 Rockchip

List of Tables

  • Table 1 Global Edge AI Processors Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global Edge AI Processors Market Outlook, By Processor Type (2024-2032) ($MN)
  • Table 3 Global Edge AI Processors Market Outlook, By CPUs (2024-2032) ($MN)
  • Table 4 Global Edge AI Processors Market Outlook, By Edge GPUs (2024-2032) ($MN)
  • Table 5 Global Edge AI Processors Market Outlook, By NPUs / Neural Engines (2024-2032) ($MN)
  • Table 6 Global Edge AI Processors Market Outlook, By Vision Processing Units (2024-2032) ($MN)
  • Table 7 Global Edge AI Processors Market Outlook, By ASICs for Edge AI (2024-2032) ($MN)
  • Table 8 Global Edge AI Processors Market Outlook, By FPGAs (2024-2032) ($MN)
  • Table 9 Global Edge AI Processors Market Outlook, By Memory Architecture (2024-2032) ($MN)
  • Table 10 Global Edge AI Processors Market Outlook, By On-Chip SRAM (2024-2032) ($MN)
  • Table 11 Global Edge AI Processors Market Outlook, By LPDDR4/LPDDR5 Integration (2024-2032) ($MN)
  • Table 12 Global Edge AI Processors Market Outlook, By HBM (High Bandwidth Memory) (2024-2032) ($MN)
  • Table 13 Global Edge AI Processors Market Outlook, By Unified Memory Access Models (2024-2032) ($MN)
  • Table 14 Global Edge AI Processors Market Outlook, By Connectivity Interface (2024-2032) ($MN)
  • Table 15 Global Edge AI Processors Market Outlook, By PCIe (2024-2032) ($MN)
  • Table 16 Global Edge AI Processors Market Outlook, By USB-C / Thunderbolt (2024-2032) ($MN)
  • Table 17 Global Edge AI Processors Market Outlook, By Ethernet / TSN (2024-2032) ($MN)
  • Table 18 Global Edge AI Processors Market Outlook, By Wi-Fi 6 / 6E / 7 (2024-2032) ($MN)
  • Table 19 Global Edge AI Processors Market Outlook, By 5G NR / mmWave (2024-2032) ($MN)
  • Table 20 Global Edge AI Processors Market Outlook, By Deployment Mode (2024-2032) ($MN)
  • Table 21 Global Edge AI Processors Market Outlook, By Embedded Devices (2024-2032) ($MN)
  • Table 22 Global Edge AI Processors Market Outlook, By Edge Gateways (2024-2032) ($MN)
  • Table 23 Global Edge AI Processors Market Outlook, By Sensors & Modules (2024-2032) ($MN)
  • Table 24 Global Edge AI Processors Market Outlook, By Robotic Systems (2024-2032) ($MN)
  • Table 25 Global Edge AI Processors Market Outlook, By Industrial Edge Nodes (2024-2032) ($MN)
  • Table 26 Global Edge AI Processors Market Outlook, By Application (2024-2032) ($MN)
  • Table 27 Global Edge AI Processors Market Outlook, By Smart Surveillance (2024-2032) ($MN)
  • Table 28 Global Edge AI Processors Market Outlook, By Autonomous Machines (2024-2032) ($MN)
  • Table 29 Global Edge AI Processors Market Outlook, By Smartphones & Wearables (2024-2032) ($MN)
  • Table 30 Global Edge AI Processors Market Outlook, By Smart Home Devices (2024-2032) ($MN)
  • Table 31 Global Edge AI Processors Market Outlook, By Industrial Automation (2024-2032) ($MN)
  • Table 32 Global Edge AI Processors Market Outlook, By End User (2024-2032) ($MN)
  • Table 33 Global Edge AI Processors Market Outlook, By Consumer Electronics (2024-2032) ($MN)
  • Table 34 Global Edge AI Processors Market Outlook, By Automotive & Mobility (2024-2032) ($MN)
  • Table 35 Global Edge AI Processors Market Outlook, By Industrial Enterprises (2024-2032) ($MN)
  • Table 36 Global Edge AI Processors Market Outlook, By Healthcare (2024-2032) ($MN)

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