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市场调查报告书
商品编码
1822472
2032 年神经型态电子市场预测:按产品、组件、部署模式、应用、最终用户和地区进行的全球分析Neuromorphic Electronics Market Forecasts to 2032 - Global Analysis By Product, Component, Deployment Mode, Application, End User and By Geography |
根据 Stratistics MRC 的数据,全球神经型态电子市场预计在 2025 年达到 1.963 亿美元,到 2032 年将达到 22.975 亿美元,预测期内复合年增长率为 42.1%。
神经型态电子学是一个工程领域,专注于设计模拟人脑结构和功能的电路和系统。这些系统使用模拟和数位元件来复製学习、记忆和模式识别等神经过程。透过模拟生物神经网络,神经型态设备提供了节能且适应性强的计算解决方案。神经型态设备在人工智慧、机器人技术和感知处理领域的应用日益广泛,其目标是透过受大脑启发的硬体架构来提升机器智慧。
节能运算的需求日益增长
传统运算架构难以满足边缘设备的效率需求,这促使各行各业探索受大脑启发的模型。神经型态晶片模拟人脑的神经结构,在维持高运算效能的同时显着降低能耗。这在医疗保健、国防和物联网等领域尤其重要,因为低延迟和低功耗运作至关重要。随着全球资料量的激增,对永续和可扩展运算解决方案的需求正在加速神经型态技术的普及。
尚未开发的软体和生态系统
儘管硬体技术发展前景光明,但由于软体框架不完善和开发工具有限,神经型态电子市场仍面临挑战。缺乏标准化的程式设计环境和模拟平台阻碍了该技术在整个产业的广泛应用。此外,与现有人工智慧模型和机器学习流程的整合仍然很复杂,需要专业知识和客製化开发。这种碎片化的生态系统减缓了创新速度,并延长了神经型态解决方案的上市时间。
非常适合自动驾驶汽车、机器人和无人机
神经型态处理器是需要在动态环境中快速决策和自适应学习的自主系统的理想选择。它们能够以极低的能耗即时处理感测数据,使其成为机器人、无人机和自动驾驶汽车的理想选择。随着各行各业向去中心化和边缘智慧迈进,神经型态电子设备为传统人工智慧加速器提供了极具吸引力的替代方案。物流、农业和国防领域对自主技术日益增长的兴趣预计将为神经型态解决方案开闢新的成长途径。
长期可靠性不确定
与传统的硅基处理器不同,神经型态晶片通常采用新型材料和架构,并且缺乏广泛的现场测试。这引发了人们对其在关键任务应用中的耐用性、容错性和扩充性的质疑。此外,由于缺乏标准化的基准和生命週期评估,相关人员难以评估风险。随着神经型态系统从实验室走向商业部署,确保强大的品质保证和可靠性指标对于赢得业界信任至关重要。
新冠疫情对神经型态电子市场产生了双重影响。供应链中断和研发预算削减暂时推迟了硬体的开发和部署。同时,这场危机加速了数位转型和远端自动化,激发了人们对智慧边缘运算的兴趣。医疗保健和製造业等行业已开始研究用于非接触式监控、预测性维护和自适应控制系统的神经型态解决方案。
预计预测期内,脉衝神经网路 (SNN) 处理器细分市场将占据最大份额
预计在预测期内,脉衝神经网路 (SNN) 处理器领域将占据最大的市场份额。这些处理器透过离散脉衝传输资讯来模拟生物神经元,从而实现非同步事件驱动的计算。这种架构显着降低了功耗,同时增强了即时回应能力,使其成为边缘设备和嵌入式系统的理想选择。 SNN 在感测处理、异常侦测和自适应控制等应用中越来越受欢迎。
语音和自然语言处理领域预计将在预测期内实现最高的复合年增长率
随着对话式人工智慧和语音介面成为主流,以及神经型态晶片为传统自然语言处理引擎提供低功耗替代方案,语音和自然语言处理领域预计将在预测期内实现最高成长率。神经形态晶片能够以极低的延迟即时处理听觉讯号,因此非常适合智慧助理、助听器和多语言翻译设备。个人化和情境感知沟通工具的需求激增,正在推动神经型态语言处理模式的创新。
在预测期内,北美预计将占据最大的市场份额,这得益于其强大的研发基础设施以及在国防、医疗保健和消费电子领域的早期应用。政府支持人工智慧创新的措施以及对自主系统的策略性投资,正在进一步推动市场成长。此外,科技巨头和创业投资的入驻也正在加速其商业化进程。北美对节能安全的运算解决方案的关注,使其成为神经型态技术部署的关键枢纽。
在预测期内,由于工业化进程加快、机器人技术应用日益普及以及智慧基础设施投资不断增加,亚太地区预计将呈现最高的复合年增长率。中国、日本和韩国等国家正积极探索神经型态解决方案,以应用于从智慧城市到智慧製造等各种应用领域。随着对边缘人工智慧和自主系统的需求不断增长,亚太地区正逐渐成为神经型态创新蓬勃发展的前沿地区。
According to Stratistics MRC, the Global Neuromorphic Electronics Market is accounted for $196.3 million in 2025 and is expected to reach $2,297.5 million by 2032 growing at a CAGR of 42.1% during the forecast period. Neuromorphic electronics is a field of engineering focused on designing circuits and systems that mimic the architecture and functionality of the human brain. These systems use analog and digital components to replicate neural processes such as learning, memory, and pattern recognition. By emulating biological neural networks, neuromorphic devices offer energy-efficient and adaptive computing solutions. They are increasingly applied in artificial intelligence, robotics, and sensory processing, aiming to enhance machine intelligence through brain-inspired hardware architectures.
Increasing need for energy-efficient computing
Traditional computing architectures struggle to meet the efficiency needs of edge devices, prompting industries to explore brain-inspired models. Neuromorphic chips, which emulate the neural structure of the human brain, offer significant reductions in energy usage while maintaining high computational performance. This is particularly valuable in sectors like healthcare, defense, and IoT, where low-latency and low-power operations are critical. As data volumes surge globally, the need for sustainable and scalable computing solutions is accelerating the adoption of neuromorphic technologies.
Immature software and ecosystem
Despite promising hardware advancements, the neuromorphic electronics market faces challenges due to underdeveloped software frameworks and limited developer tools. The lack of standardized programming environments and simulation platforms hinders widespread implementation across industries. Moreover, integration with existing AI models and machine learning pipelines remains complex, requiring specialized knowledge and custom development. This fragmented ecosystem slows down innovation and increases the time-to-market for neuromorphic solutions.
Ideal for autonomous vehicles, robotics, and drones
Neuromorphic processors are uniquely suited for autonomous systems that demand rapid decision-making and adaptive learning in dynamic environments. Their ability to process sensory data in real time with minimal energy makes them ideal for robotics, drones, and self-driving vehicles. As industries push toward decentralization and edge intelligence, neuromorphic electronics offer a compelling alternative to conventional AI accelerators. The growing interest in autonomous technologies across logistics, agriculture, and defense is expected to unlock new growth avenues for neuromorphic solutions.
Uncertain long-term reliability
Unlike traditional silicon-based processors, neuromorphic chips often use novel materials and architectures that lack extensive field testing. This raises questions about their durability, error tolerance, and scalability in mission-critical applications. Additionally, the absence of standardized benchmarks and lifecycle assessments makes it difficult for stakeholders to evaluate risk. As neuromorphic systems move from research labs to commercial deployment, ensuring robust quality assurance and reliability metrics will be essential to gain industry trust.
The COVID-19 pandemic had a dual impact on the neuromorphic electronics market. On one hand, supply chain disruptions and reduced R&D budgets temporarily slowed hardware development and deployment. On the other hand, the crisis accelerated digital transformation and remote automation, increasing interest in intelligent edge computing. Sectors like healthcare and manufacturing began exploring neuromorphic solutions for contactless monitoring, predictive maintenance, and adaptive control systems.
The spiking neural network (SNN) processors segment is expected to be the largest during the forecast period
The spiking neural network (SNN) processors segment is expected to account for the largest market share during the forecast period as these processors mimic biological neurons by transmitting information through discrete spikes, enabling asynchronous and event-driven computation. Their architecture significantly reduces power consumption while enhancing real-time responsiveness, making them ideal for edge devices and embedded systems. SNNs are gaining traction in applications such as sensory processing, anomaly detection, and adaptive control.
The speech & natural language processing segment is expected to have the highest CAGR during the forecast period
Over the forecast period, the speech & natural language processing segment is predicted to witness the highest growth rate because conversational AI and voice-enabled interfaces become mainstream, neuromorphic chips offer a low-power alternative to traditional NLP engines. Their ability to process auditory signals in real time with minimal latency makes them suitable for smart assistants, hearing aids, and multilingual translation devices. The surge in demand for personalized and context-aware communication tools is driving innovation in neuromorphic NLP models.
During the forecast period, the North America region is expected to hold the largest market share driven by robust R&D infrastructure and early adoption across defense, healthcare, and consumer electronics. Government initiatives supporting AI innovation and strategic investments in autonomous systems are further boosting market growth. Additionally, the presence of tech giants and venture capital funding is accelerating commercialization efforts. North America's strong emphasis on energy-efficient and secure computing solutions positions it as a key hub for neuromorphic technology deployment.
Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR fueled by rapid industrialization, expanding robotics adoption, and increasing investments in smart infrastructure. Countries like China, Japan, and South Korea are actively exploring neuromorphic solutions for applications ranging from smart cities to intelligent manufacturing. As demand for edge AI and autonomous systems rises, Asia Pacific is emerging as a vibrant growth frontier for neuromorphic innovation.
Key players in the market
Some of the key players in Neuromorphic Electronics Market include Intel Corporation, IBM Corporation, Qualcomm Technologies, Inc., BrainChip Holdings Ltd., Samsung Electronics Co., Ltd., GrAI Matter Labs, Innatera Nanosystems B.V., General Vision Inc., SynSense AG, HRL Laboratories, LLC, NVIDIA Corporation, SK hynix Inc., Applied Brain Research, Inc., Prophesee SA, Mythic Inc., MemryX Inc., Knowm Inc., Polyn Technology, Hewlett Packard Enterprise (HPE) and Vicarious Corp.
In September 2025, NVIDIA invested $5B in Intel and announced joint development of AI infrastructure and PC chips. Intel will manufacture custom CPUs integrated with NVIDIA's NVLink and RTX GPU chiplets.
In July 2025, HRL released spinQICK, an open-source extension for controlling solid-state spin-qubits using affordable FPGA hardware. It enables rapid development of quantum computing experiments and supports academic outreach.
In February 2025, SynSense acquired 100% of iniVation to form the world's first fully neuromorphic end-to-end sensing and processing company. The merger combines vision sensors and processors for robotics, aerospace, and consumer electronics.
Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.