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市场调查报告书
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1896145

边缘人工智慧处理器市场预测至2032年：按处理器类型、记忆体架构、连接介面、部署模式、应用、最终用户和地区分類的全球分析

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个工作天内

价格

根据 Stratistics MRC 的一项研究，预计到 2025 年，全球边缘 AI 处理器市场价值将达到 43 亿美元，到 2032 年将达到 78 亿美元，预测期内复合年增长率为 8.8%。

边缘人工智慧处理器是先进的半导体晶片，旨在直接在本地设备上执行人工智慧任务，从而无需依赖远端云端伺服器。它们整合了加速器和优化的记忆体层次结构，能够为自动驾驶、工业IoT、机器人和智慧监控等关键应用提供高效能运算，实现即时决策。透过最大限度地降低延迟、减少频宽使用并增强资料隐私，这些处理器能够实现更快、更安全、更有效率的运行，使其成为下一代智慧互联繫统的重要组成部分。

自主系统和物联网的发展

自主系统和物联网设备的快速扩张正推动着对边缘人工智慧处理器的强劲需求。这些晶片能够直接在本地设备上进行即时决策，从而降低延迟并减少对云端基础设施的依赖。其应用涵盖自动驾驶汽车、工业机器人、智慧监控、连线健诊医疗以及其他对即时回应至关重要的领域。随着全球数十亿物联网终端的激增，边缘人工智慧处理器对于建立下一代互联生态系统至关重要，它们能够提供可扩展的智能，并确保效率、安全性和响应速度。

分段式软体和工具链支持

儘管硬体不断进步，但软体生态系统的割裂和工具链支援的不足仍然是边缘人工智慧处理器发展的主要限制因素。开发者在优化跨架构工作负载方面面临许多挑战，导致效率低和推广缓慢。缺乏标准化框架使得与现有系统的整合变得复杂，而专有解决方案则增加了成本并限制了互通性。这种割裂阻碍了可扩展性，抑制了中小企业的发展，并减缓了创新。如果没有统一的平台和强大的开发者支持，边缘人工智慧处理器将面临无法充分利用、无法在关键即时应用中发挥其真正潜力的风险。

边缘云端混合编配平台

边缘云端混合编配平台为边缘人工智慧处理器带来了变革性的机会。透过结合本地推理和云端分析，这些系统能够提供最佳化的效能、可扩展性和柔软性。企业可以在边缘处理敏感数据，从而保障隐私和速度，同时利用云端资源获得更深入的洞察和模型训练。这种混合方法支援从智慧城市到自动驾驶车队等各种应用场景，并可在分散式环境中实现无缝协作。这使得边缘人工智慧处理器成为未来智慧基础设施的核心。

边缘部署中的安全漏洞

边缘环境的安全漏洞对边缘人工智慧处理器市场构成重大威胁。分散式架构增加了遭受网路攻击、资料外洩和恶意干扰的风险。与集中式云端系统不同，边缘设备通常缺乏强大的安全通讯协定，使其成为攻击的理想目标。一旦处理器遭到入侵，可能会扰乱自动驾驶、工业IoT网路和医疗保健系统，造成严重后果。应对这些风险需要先进的加密技术、安全启动机制和持续监控。如果没有强而有力的保护措施，边缘人工智慧的普及可能会停滞不前，人们对边缘智慧的信任度也会下降。

新冠疫情的影响

新冠疫情加速了数位转型和远距办公，推动了医疗保健、监控和工业自动化领域对边缘人工智慧处理器的需求。部分地区云端存取限制使得边缘运算在即时、隐私敏感型任务中变得更加重要。然而，晶片短缺和生产中断影响了供应，导致产品发布延迟。疫情凸显了分散式智慧的重要性，推动了对用于自主系统、智慧城市和非接触式技术的边缘人工智慧的投资。该市场被视为后疫情时代韧性的关键基础。

预计在预测期内，边缘人工智慧专用积体电路 (ASIC) 细分市场将占据最大的市场份额。

由于其架构专注于高效推理和低功耗，边缘人工智慧专用积体电路（ASIC）预计将在预测期内占据最大的市场份额。这些晶片针对特定的人工智慧工作负载提供最佳化的性能，从而支援重型电动车（​​EV）的即时决策。它们的整合支援高级驾驶辅助系统（ADAS）、预测性维护和自动驾驶功能。 ASIC 的可扩展性和成本效益使其成为寻求每瓦性能优势的原始设备製造商（OEM）的理想选择，推动了其在商用电动车平台上的应用。

预计在预测期内，LPDDR4/LPDDR5一体化记忆体细分市场将呈现最高的复合年增长率。

预计在预测期内，LPDDR4/LPDDR5整合记忆体市场将保持最高的成长率，这主要得益于其高频宽和低功耗的完美平衡。这些记忆体类型对于电动车动力传动系统中的即时感测器资料处理、人工智慧推理和多媒体处理至关重要。其紧凑的尺寸和优异的散热性能使其非常适合在资源受限的边缘环境中部署。随着电动车向智慧互联平台演进，对基于LPDDR的记忆体架构的需求预计将大幅成长，尤其是在需要快速启动和低延迟的应用中。

比最大的地区

亚太地区预计将在整个预测期内保持最大的市场份额，这主要得益于中国、日本和韩国强有力的政府支持政策、快速的都市化以及积极的电气化目标。该地区拥有强大的製造业生态系统、成本效益高的劳动力以及大规模的电动车生产能力。对电池技术、充电基础设施和人工智慧驱动的出行解决方案的策略性投资进一步巩固了其优势。亚太地区的整车製造商和一级供应商正在加速创新，使该地区成为重型电动车动力传动系统领域的成长中心。

预计年复合成长率最高的地区

在预测期内，北美地区预计将实现最高的复合年增长率，这主要得益于严格的排放法规、车队电气化强制令以及对可持续物流日益增长的需求。联邦和州政府层级的奖励正在推动商用车队采用电动车，尤其是在最后一公里配送和远距货运领域。对人工智慧驱动的车辆智慧的重视，以及电池和温度控管系统的进步，正在促进电动车的快速部署。汽车製造商、科技公司和公共产业公司之间的合作，为下一代电动车动力传动系统系统的创新创造了沃土。

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目录

第一章执行摘要

第二章 前言

  • 摘要
  • 相关利益者
  • 调查范围
  • 调查方法
  • 研究材料

第三章 市场趋势分析

  • 司机
  • 抑制因素
  • 机会
  • 威胁
  • 应用分析
  • 终端用户分析
  • 新兴市场
  • 新冠疫情的感染疾病

第四章 波特五力分析

  • 供应商的议价能力
  • 买方的议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争对手之间的竞争

5. 全球边缘人工智慧处理器市场（按处理器类型划分）

  • CPU
  • 边缘 GPU
  • NPU/神经网路引擎
  • 视觉处理单元
  • 用于边缘人工智慧的专用积体电路 (ASIC)
  • FPGA

6. 全球边缘人工智慧处理器市场（按记忆体架构划分）

  • 片上SRAM
  • LPDDR4/LPDDR5 集成
  • HBM（高频宽记忆体）
  • 统一记忆体存取模型

7. 全球边缘人工智慧处理器市场（依连接介面划分）

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

8. 全球边缘人工智慧处理器市场依部署模式划分

  • 嵌入式装置
  • 边缘网关
  • 感测器和模组
  • 机器人系统
  • 工业边缘节点

第九章 全球边缘人工智慧处理器市场（按应用划分）

  • 智慧监控
  • 自主机器
  • 智慧型手机和穿戴式装置
  • 智慧家庭设备
  • 工业自动化

第十章 全球边缘人工智慧处理器市场（按最终用户划分）

  • 家用电器
  • 汽车出行
  • 工业公司
  • 医疗保健

第十一章 全球边缘人工智慧处理器市场（按地区划分）

  • 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
  • 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
    • 其他南美国家
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十二章 重大进展

  • 协议、伙伴关係、合作和合资企业
  • 併购
  • 新产品发布
  • 业务拓展
  • 其他关键策略

第十三章：企业概况

  • 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.