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1871080

用于自动驾驶汽车的神经形态晶片市场机会、成长驱动因素、产业趋势分析及预测（2025-2034年）

Neuromorphic Chips for Autonomous Vehicles Market Opportunity, Growth Drivers, Industry Trend Analysis, and Forecast 2025 - 2034

出版日期: 2025年10月18日 | 出版商:

Global Market Insights Inc. | 英文 190 Pages | 商品交期: 2-3个工作天内

价格

简介目录

2024 年全球自动驾驶汽车神经形态晶片市值为 91.1 亿美元，预计到 2034 年将以 20.7% 的复合年增长率增长至 591.6 亿美元。

对自动驾驶汽车日益增长的需求推动了对能够快速处理大量感测器输入的先进运算系统的需求。模拟人脑结构的神经形态晶片，相较于传统处理器，能够实现更快的决策速度和更高的能源效率。随着车辆整合多个摄影机、光达和雷达技术以提升安全性和可靠性，对瞬时资料处理的需求也持续成长。随着电动车和自动驾驶汽车的发展，能源优化和散热管理已成为关键问题。传统的GPU和CPU在持续执行AI任务时经常面临过热降频的问题，这限制了其可扩展性。相较之下，神经形态处理器利用平行事件驱动运算仅处理相关资料，从而显着降低功耗并提升电动车的电池效能。感测器系统的进步进一步推动了神经形态技术的广泛应用。新一代仿生和事件驱动型感测器能够产生针对神经形态处理最佳化的资料流，从而实现传统架构无法实现的非同步和脉衝驱动资料处理。

市场范围
起始年份	2024
预测年份	2025-2034
起始值	91.1亿美元
预测值	591.6亿美元
复合年增长率	20.7%

2024年，数位神经形态晶片市占率达43.2%。其主导地位源于与现有汽车人工智慧系统和电子控制单元的无缝集成，使汽车製造商无需进行重大重新设计即可实现应用。与现有人工智慧框架的兼容性，以及顶级半导体製造商的大力支持，确保了该领域的可扩展性、可靠性和持续创新。

由于车载（边缘）部署模式在即时感测器资料处理中发挥关键作用，预计到2024年，该模式将创造39.5亿美元的市场规模。边缘运算使车辆能够在本地分析讯息，从而消除延迟，并实现对道路和交通状况的即时响应。这种架构对于需要瞬间决策的安全应用至关重要，例如自动煞车和碰撞预防。

预计到2024年，美国用于自动驾驶汽车的神经形态晶片市场规模将达25亿美元。美国持续受益于公共和私营部门对人工智慧和自动驾驶技术的强劲投资。强大的创新生态系统和众多关键技术公司的存在，推动了用于高速即时决策的神经形态计算技术的持续研究。美国对智慧节能汽车系统的日益重视，进一步加速了神经形态晶片在汽车产业的应用。

全球自动驾驶汽车神经形态晶片市场的主要参与者包括埃森哲、Applied Brain Research Inc.、Aspinity Inc.、博格华纳、BrainChip Holdings Ltd.、Cadence Design Systems Inc.、Figaro Engineering Inc.、General Vision Inc.、Grayscale AI、Gyrfalcon Technology Inc.、惠普企业发展有限公司、IBMem、Mem. Inc.、英伟达公司、Polyn Technology、Prophesee SA、高通技术公司、三星电子有限公司和索尼公司。为了巩固自身地位，自动驾驶汽车神经形态晶片市场的领导者正优先考虑策略合作、产品创新和可扩展的生产製造。许多企业正大力投资研发，以提高晶片效率、降低功耗并提升资料处理精度。半导体公司与汽车製造商之间的合作正在加速下一代汽车系统的整合和测试。一些企业也致力于扩大其地理覆盖范围，并与人工智慧软体开发商结盟，以使神经形态技术与新兴的汽车标准保持一致。

产业生态系分析
- 供应商格局
- 利润率
- 成本结构
- 每个阶段的价值增加
- 影响价值链的因素
- 中断
衝击力
- 成长驱动因素
  - 先进自动驾驶汽车的采用
  - 节能运算与边缘人工智慧
  - 汽车感测器集成
  - 产学合作与研发
  - 先进自动驾驶汽车的采用
- 产业陷阱与挑战
  - 高昂的开发和实施成本
  - 复杂系统集成
成长潜力分析
监管环境
- 北美洲
- 欧洲
- 亚太地区
- 拉丁美洲
- 中东和非洲
波特的分析
PESTEL 分析
技术与创新格局
- 当前技术趋势
- 新兴技术
新兴商业模式
合规要求
消费者情绪分析
专利和智慧财产权分析
地缘政治与贸易动态

第四章：竞争格局

介绍
公司市占率分析
- 按地区
- 市场集中度分析
对主要参与者进行竞争基准分析
- 财务绩效比较
  - 收入
  - 利润率
  - 研发
- 产品组合比较
  - 产品范围广度
  - 科技
  - 创新
- 地理位置比较
  - 全球足迹分析
  - 服务网路覆盖
  - 按地区分類的市场渗透率
- 竞争定位矩阵
  - 领导人
  - 挑战者
  - 追踪者
  - 小众玩家
- 战略展望矩阵
2021-2024 年主要发展动态
- 併购
- 伙伴关係与合作
- 技术进步
- 扩张和投资策略
- 数位转型计划
新兴/新创企业竞争对手格局

第五章：市场估算与预测：依晶片架构划分，2021-2034年

主要趋势
模拟
数位的
混合讯号

第六章：市场估算与预测：依部署方式划分，2021-2034年

主要趋势
板载（边缘）处理
云端辅助处理
混合处理

第七章：市场估价与预测：依车辆类别划分，2021-2034年

主要趋势
搭乘用车
- 商用车辆
- 卡车
- 公车
自动驾驶接驳车和无人驾驶计程车
越野及特种车辆
- 农业
- 矿业
- 建造
其他的

第八章：市场估算与预测：依最终用途划分，2021-2034年

主要趋势
汽车原厂设备製造商
一级供应商
售后解决方案提供商
研究与开发实体
其他的

第九章：市场估计与预测：依地区划分，2021-2034年

主要趋势
北美洲
- 我们
- 加拿大
欧洲
- 英国
- 德国
- 法国
- 义大利
- 西班牙
- 荷兰
亚太地区
- 中国
- 印度
- 日本
- 韩国
- 澳洲
拉丁美洲
- 巴西
- 墨西哥
- 阿根廷
MEA
- 南非
- 沙乌地阿拉伯
- 阿联酋

第十章：公司简介

Accenture
Applied Brain Research Inc.
Aspinity Inc.
BorgWarner Inc.
BrainChip Holdings Ltd.
Cadence Design Systems, Inc.
Figaro Engineering Inc.
General Vision Inc.
Grayscale AI
Gyrfalcon Technology Inc.
Hewlett Packard Enterprise Development LP
IBM Corporation
Innatera Nanosystems BV
Intel Corporation
MemryX Inc.
Micron Technology, Inc.
Mythic Inc.
NVIDIA Corporation
Polyn Technology
Prophesee SA
Qualcomm Technologies, Inc.
Samsung Electronics Co., Ltd.
Sony Corporation
SynSense AG
Syntiant Corp.
Vicarious Corp.
Vivum Computing

简介目录

Product Code: 14960

The Global Neuromorphic Chips for Autonomous Vehicles Market was valued at USD 9.11 Billion in 2024 and is estimated to grow at a CAGR of 20.7% to reach USD 59.16 Billion by 2034.

Neuromorphic Chips for Autonomous Vehicles Market - IMG1

The accelerating demand for self-driving vehicles is driving the need for advanced computing systems that can rapidly process vast amounts of sensory input. Neuromorphic chips, which emulate the human brain's structure, enable faster decision-making and greater energy efficiency than traditional processors. As vehicles integrate multiple cameras, LiDAR, and radar technologies to enhance safety and reliability, the requirement for instantaneous data processing continues to rise. With the evolution of electric and autonomous vehicles, energy optimization and heat management have become key concerns. Conventional GPUs and CPUs often face thermal throttling during continuous AI tasks, which limits scalability. In contrast, neuromorphic processors handle only relevant data using parallel, event-driven computation, which significantly reduces power consumption and improves battery performance in electric vehicles. The growing adoption of neuromorphic technology is further supported by advancements in sensor systems. Next-generation bio-inspired and event-based sensors produce data streams optimized for neuromorphic processing, enabling asynchronous and spike-driven data handling that is not possible with conventional architectures.

Market Scope
Start Year	2024
Forecast Year	2025-2034
Start Value	$9.11 billion
Forecast Value	$59.16 billion
CAGR	20.7%

In 2024, the digital neuromorphic chips segment accounted for a 43.2% share. Their dominance stems from seamless integration with existing automotive AI systems and electronic control units, allowing automakers to implement them without major redesigns. Their compatibility with current AI frameworks, along with strong support from top semiconductor manufacturers, ensures scalability, reliability, and consistent innovation in this segment.

The on-board (edge) deployment model generated USD 3.95 Billion in 2024, owing to its critical role in real-time sensor data processing. Edge computing allows vehicles to analyze information locally, eliminating latency and enabling instantaneous response to road and traffic conditions. This architecture is crucial for safety applications that require split-second decision-making, such as automated braking and collision prevention.

United States Neuromorphic Chips for Autonomous Vehicles Market generated USD 2.5 Billion in 2024. The U.S. continues to benefit from robust investment in artificial intelligence and autonomous driving technologies, supported by both public and private sectors. A strong innovation ecosystem and the presence of key technology firms contribute to ongoing research in neuromorphic computing for high-speed, real-time decision-making. The nation's growing focus on intelligent and energy-efficient vehicle systems further accelerates the integration of neuromorphic chips in the automotive industry.

Key companies active in the Global Neuromorphic Chips for Autonomous Vehicles Market include Accenture, Applied Brain Research Inc., Aspinity Inc., BorgWarner Inc., BrainChip Holdings Ltd., Cadence Design Systems Inc., Figaro Engineering Inc., General Vision Inc., Grayscale AI, Gyrfalcon Technology Inc., Hewlett Packard Enterprise Development LP, IBM Corporation, Intel Corporation, MemryX Inc., Micron Technology Inc., Mythic Inc., NVIDIA Corporation, Polyn Technology, Prophesee SA, Qualcomm Technologies Inc., Samsung Electronics Co. Ltd., and Sony Corporation. To strengthen their position, leading companies in the Neuromorphic Chips for Autonomous Vehicles Market are prioritizing strategic collaborations, product innovation, and scalable manufacturing. Many are investing heavily in R&D to enhance chip efficiency, reduce power usage, and improve data processing accuracy. Partnerships between semiconductor firms and automotive manufacturers are accelerating integration and testing within next-generation vehicle systems. Several players are also focusing on expanding their geographic reach and forming alliances with AI software developers to align neuromorphic technology with emerging automotive standards.

Chapter 1 Methodology & Scope

1.1 Market scope and definition
1.2 Research design
- 1.2.1 Research approach
- 1.2.2 Data collection methods
1.3 Data mining sources
- 1.3.1 Global
- 1.3.2 Regional/Country
1.4 Base estimates and calculations
- 1.4.1 Base year calculation
- 1.4.2 Key trends for market estimation
1.5 Primary research and validation
- 1.5.1 Primary sources
1.6 Forecast model
1.7 Research assumptions and limitations

Chapter 2 Executive Summary

2.1 Industry 360⁰ synopsis
2.2 Key market trends
- 2.2.1 Chip Architecture trends
- 2.2.2 Deployment trends
- 2.2.3 Vehicle Category trends
- 2.2.4 End use trends
- 2.2.5 Regional trends
2.3 TAM Analysis, 2025-2034 (USD Billion)
2.4 CXO perspectives: Strategic imperatives
- 2.4.1 Executive decision points
- 2.4.2 Critical success factors
2.5 Future outlook and strategic recommendations

Chapter 3 Industry Insights

3.1 Industry ecosystem analysis
- 3.1.1 Supplier landscape
- 3.1.2 Profit margin
- 3.1.3 Cost structure
- 3.1.4 Value addition at each stage
- 3.1.5 Factor affecting the value chain
- 3.1.6 Disruptions
3.2 Impact forces
- 3.2.1 Growth drivers
  - 3.2.1.1 Advanced Autonomous Vehicle Adoption
  - 3.2.1.2 Energy-Efficient Computing & Edge AI
  - 3.2.1.3 Automotive Sensor Integration
  - 3.2.1.4 Industry-Academia Collaborations & R&D
  - 3.2.1.5 Advanced Autonomous Vehicle Adoption
- 3.2.2 Industry pitfalls & challenges
  - 3.2.2.1 High Development and Implementation Costs
  - 3.2.2.2 Complex System Integration
3.3 Growth potential analysis
3.4 Regulatory landscape
- 3.4.1 North America
- 3.4.2 Europe
- 3.4.3 Asia Pacific
- 3.4.4 Latin America
- 3.4.5 Middle East & Africa
3.5 Porter's analysis
3.6 PESTEL analysis
3.7 Technology and innovation landscape
- 3.7.1 Current technological trends
- 3.7.2 Emerging technologies
3.8 Emerging business models
3.9 Compliance requirements
3.10 Consumer sentiment analysis
3.11 Patent and IP analysis
3.12 Geopolitical and trade dynamics

Chapter 4 Competitive Landscape, 2024

4.1 Introduction
4.2 Company market share analysis
- 4.2.1 By region
  - 4.2.1.1 North America
  - 4.2.1.2 Europe
  - 4.2.1.3 Asia Pacific
  - 4.2.1.4 Latin America
  - 4.2.1.5 MEA
- 4.2.2 Market concentration analysis
4.3 Competitive benchmarking of key players
- 4.3.1 Financial performance comparison
  - 4.3.1.1 Revenue
  - 4.3.1.2 Profit margin
  - 4.3.1.3 R&D
- 4.3.2 Product portfolio comparison
  - 4.3.2.1 Product range breadth
  - 4.3.2.2 Technology
  - 4.3.2.3 Innovation
- 4.3.3 Geographic presence comparison
  - 4.3.3.1 Global footprint analysis
  - 4.3.3.2 Service network coverage
  - 4.3.3.3 Market penetration by region
- 4.3.4 Competitive positioning matrix
  - 4.3.4.1 Leaders
  - 4.3.4.2 Challengers
  - 4.3.4.3 Followers
  - 4.3.4.4 Niche players
- 4.3.5 Strategic outlook matrix
4.4 Key developments, 2021-2024
- 4.4.1 Mergers and acquisitions
- 4.4.2 Partnerships and collaborations
- 4.4.3 Technological advancements
- 4.4.4 Expansion and investment strategies
- 4.4.5 Digital Transformation Initiatives
4.5 Emerging/ Startup Competitors Landscape

Chapter 5 Market Estimates & Forecast, By Chip Architecture, 2021-2034 (USD Billion)

5.1 Key trends
5.2 Analog
5.3 Digital
5.4 Mixed-Signal

Chapter 6 Market Estimates & Forecast, By Deployment, 2021-2034 (USD Billion)

6.1 Key trends
6.2 On-Board (Edge) Processing
6.3 Cloud-Assisted Processing
6.4 Hybrid Processing

Chapter 7 Market Estimates & Forecast, By Vehicle Category, 2021-2034 (USD Billion)

7.1 Key trends
7.2 Passenger Cars
- 7.2.1 Commercial Vehicles
- 7.2.2 Trucks
- 7.2.3 Buses
7.3 Autonomous Shuttles & Robo-Taxis
7.4 Off-Road & Specialized Vehicles
- 7.4.1 Agriculture
- 7.4.2 Mining
- 7.4.3 Construction
7.5 Others

Chapter 8 Market Estimates & Forecast, By End Use, 2021-2034 (USD Billion)

8.1 Key trends
8.2 Automotive OEMs
8.3 Tier-1 Suppliers
8.4 Aftermarket Solution Providers
8.5 Research & Development Entities
8.6 Others

Chapter 9 Market Estimates & Forecast, By Region, 2021-2034 (USD Billion)

9.1 Key trends
9.2 North America
- 9.2.1 U.S.
- 9.2.2 Canada
9.3 Europe
- 9.3.1 UK
- 9.3.2 Germany
- 9.3.3 France
- 9.3.4 Italy
- 9.3.5 Spain
- 9.3.6 Netherlands
9.4 Asia Pacific
- 9.4.1 China
- 9.4.2 India
- 9.4.3 Japan
- 9.4.4 South Korea
- 9.4.5 Australia
9.5 Latin America
- 9.5.1 Brazil
- 9.5.2 Mexico
- 9.5.3 Argentina
9.6 MEA
- 9.6.1 South Africa
- 9.6.2 Saudi Arabia
- 9.6.3 UAE