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市场调查报告书
商品编码
1935824

全球神经形态计算与感测市场（2026-2036 年）

The Global Neuromorphic Computing & Sensing Market 2026-2036
出版日期: | 出版商: Future Markets, Inc. | 英文 367 Pages, 83 Tables, 63 Figures | 订单完成后即时交付

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全球神经型态运算和感测市场是半导体发展领域最具变革性的新兴领域之一，与传统数位运算和量子运算并驾齐驱，成为「第三大趋势」。这项受大脑启发的技术采用与传统冯·诺依曼架构截然不同的架构来处理讯息，透过将记忆体和处理单元置于同一位置，消除了传统CPU和GPU性能受限的、耗能巨大的往返资料传输。国际能源总署（IEA）预测，到2030年，资料中心的电力消耗的3%，这主要是由于神经网路模拟所需的运算资源。神经型态计算透过在硬体上实现神经网络，而非透过二元序列定序，直接应对了这项永续性挑战。英特尔的神经型态处理器Loihi 2在某些推理任务中展现出比传统处理器高出100倍的能效，而BrainChip的Akida Pulsar的能耗则比传统AI核心降低了500倍。

竞争格局呈现多元化的生态系统，既有成熟的科技巨头，也有创新Start-Ups。英特尔的Hala Point系统将于2024年在桑迪亚国家实验室部署，它是全球最大的神经型态平台，拥有1152个Loihi 2处理器和11.5亿个神经元。 IBM的基础技术TrueNorth正透过神经突触研究不断发展，而BrainChip已成功将其Akida处理器商业化，应用于全球数百万台物联网设备。欧洲企业正透过英国多学科神经形态运算中心等专案加速发展，而包括SynSense和华为在内的中国企业则在物联网和智慧城市领域大力推动应用。

推动神经形态晶片普及的关键应用领域包括边缘人工智慧和物联网。神经型态晶片能够使智慧感测器、无人机和自动驾驶汽车以极低的电力消耗进行即时决策。在医疗应用领域，例如携带式诊断设备、可检测心率异常的可穿戴监测器以及能够实现人机无缝通讯的脑机介面。网路安全领域预计将即时商业性应用，因为神经型态系统擅长侦测网路流量中的细微异常。在金融服务领域，神经形态晶片被用于分析高频交易和检测复杂资料流中的诈欺行为；而在工业应用领域，则包括预测性维护、品质检测和供应链优化。

儘管市场前景可期，但仍面临诸多挑战，例如可扩展性限制、与现有基础设施整合的复杂性以及对标准化程式框架的需求。与传统运算相比，软体生态系统仍不发达，开发针对神经型态硬体最佳化的演算法需要一种全新的方法。然而，随着数位神经型态设计（作为模拟实现的替代方案）的进步以及诸如神经形态中间表示（NIR）等标准化工作的推进，这些障碍正逐步被克服。

人工智慧工作负载的爆炸性增长、边缘设备的激增以及对能源永续性日益增长的需求正在汇聚，使神经型态运算正处于关键的转折点。随着这项技术从实验室走向商业产品，其实现更智慧、更适应、更节能的运算的潜力表明，神经型态系统将在2035年后不断发展的人工智慧领域中扮演越来越重要的角色。

本报告对全球神经形态计算和感测市场进行了全面分析，并按技术类型、应用领域和地区提供了详细的市场预测。

目录

第一章执行摘要

  • 神经型态计算与感测市场概览
  • 摩尔定律的终结
  • 市场表现
  • 市场（2024）
  • 主要市场趋势和成长驱动因素
  • 市场挑战与局限性
  • 关键业界考察
  • 未来展望与机会
  • 主要产品发布计画（2024-2025）
  • 神经型态运算与生成式人工智慧
  • 市场价值链
  • 市场地图
  • 资金筹措及投资
  • 策略伙伴关係和联盟
  • 监管和伦理方面的考量
  • 永续性和环境影响

第二章：引言

  • 神经型态计算与感知的定义与概念
  • 主要神经型态方法
  • 神经型态系统的製造工艺
  • 主要材料供应商
  • 供应链脆弱性及因应措施
  • 生产能力分析
  • 品管和测试方法
  • 与传统计算和感测方法的比较
  • 神经型态计算与量子计算的比较
  • 主要特点和优势
  • 市场与应用

第三章：神经型态运算的技术与架构

  • 脉衝神经网路（SNN）
  • 神经型态运算的记忆体架构
  • 神经型态硬体处理器
  • 用于神经型态计算的软体框架

第四章：神经型态技术与架构

  • 基于事件的传感器和处理
  • 混合感测方法
  • 神经型态感测器架构与设计
  • 讯号处理和特征提取技术

第五章 市场分析与预测

  • 商业发展亮点（2025 年）
  • 行动和消费者使用
  • 汽车和交通运输
  • 工业和製造业
  • 医疗及医疗设备
  • 航太/国防
  • 资料中心云端服务
  • 市场分析与预测：按地区划分
  • 竞争格局和主要企业
  • 相互竞争的新技术

第六章：公司简介（151家公司简介）

第七章：调查方法

第八章参考文献

The Global Neuromorphic Computing and Sensing Market represents one of the most transformative frontiers in semiconductor development, emerging as the "third stream" alongside traditional digital and quantum computing paradigms. This brain-inspired technology processes information through architectures that fundamentally depart from conventional von Neumann designs, co-locating memory and processing units to eliminate the energy-intensive data shuttling that limits traditional CPU and GPU performance. According to the International Energy Agency, data centres could consume 3% of global electricity by 2030, primarily driven by the computational demands of simulating neural networks. Neuromorphic computing directly addresses this sustainability challenge by implementing neural networks in hardware rather than simulating them through binary sequences. Intel's Loihi 2 neuromorphic processor has demonstrated energy savings of up to 100x over conventional processors for certain inference tasks, while BrainChip's Akida Pulsar delivers 500x lower energy consumption compared to traditional AI cores.

The competitive landscape features a diverse ecosystem spanning established technology giants and innovative startups. Intel's Hala Point system, deployed at Sandia National Laboratories in 2024, represents the world's largest neuromorphic platform with 1.15 billion neurons across 1,152 Loihi 2 processors. IBM's foundational TrueNorth technology continues advancing through neurosynaptic research, while BrainChip has achieved commercial deployment of its Akida processor in millions of IoT devices globally. European players are accelerating through initiatives like the UK Multidisciplinary Centre for Neuromorphic Computing, while Chinese companies including SynSense and Huawei are driving significant IoT and smart city applications.

Key application verticals driving adoption include edge AI and IoT, where neuromorphic chips enable smart sensors, drones, and autonomous vehicles to make real-time decisions with minimal power consumption. Healthcare applications span portable diagnostic devices, wearable monitors detecting cardiac anomalies, and brain-computer interfaces enabling more seamless human-machine communication. Cybersecurity represents an area of immediate commercial viability, with neuromorphic systems excelling at detecting subtle anomalies in network traffic. Financial services benefit from high-frequency trading analysis and fraud detection in complex data streams, while industrial applications encompass predictive maintenance, quality inspection, and supply chain optimization.

Despite promising growth, the market faces meaningful challenges including scalability constraints, integration complexities with existing infrastructure, and the need for standardised programming frameworks. The software ecosystem remains underdeveloped compared to conventional computing, and developing algorithms optimised for neuromorphic hardware requires fundamentally new approaches. However, advances in digital neuromorphic designs replacing analog implementations, alongside standardisation efforts like the Neuromorphic Intermediate Representation, are progressively addressing these barriers.

The convergence of exploding AI workloads, edge device proliferation, and growing energy sustainability requirements positions neuromorphic computing at a critical inflection point. As the technology transitions from research laboratories to commercial products, its potential to enable more intelligent, adaptive, and energy-efficient computation suggests neuromorphic systems will play an increasingly central role in the evolving AI landscape through 2035 and beyond.

The Global Neuromorphic Computing & Sensing Market 2026-2036 provides comprehensive analysis of the rapidly evolving brain-inspired computing industry, now recognized as the "third stream" of semiconductor development alongside digital and quantum technologies. This definitive market intelligence report delivers actionable insights for investors, technology strategists, and industry stakeholders seeking to capitalize on one of the fastest-growing segments in artificial intelligence hardware.

Neuromorphic computing represents a paradigm shift in how machines process information, drawing direct inspiration from biological neural networks to achieve unprecedented energy efficiency and real-time processing capabilities. With data centres projected to consume 3% of global electricity by 2030 due to conventional AI workloads, neuromorphic technology offers a sustainable pathway forward. This extensively researched report examines the complete neuromorphic ecosystem spanning hardware, software, sensors, and applications. The analysis covers spiking neural networks, emerging non-volatile memory technologies including Phase-Change Memory, Resistive RAM, Magnetoresistive RAM, and Ferroelectric RAM, alongside detailed assessment of digital, analog, and mixed-signal neuromorphic processor architectures.

The report delivers granular market forecasts segmented by technology type, application vertical, and geographic region through 2036. Key application sectors analyzed include mobile and consumer electronics, automotive and transportation, industrial manufacturing, healthcare and medical devices, aerospace and defense, and datacenter infrastructure. Regional analysis encompasses North America, Europe, Asia-Pacific, and Rest of World markets with country-level insights.

Critical technology developments are thoroughly examined, including Intel's landmark Hala Point system featuring 1.15 billion neurons, Innatera's sub-milliwatt T1 processor, BrainChip's Akida Pulsar delivering 500x energy reduction, and the Chinese Academy of Sciences' SpikingBrain-1.0 model. The software ecosystem analysis covers Intel's Lava framework, Neuromorphic Intermediate Representation standardization efforts, and PyTorch-based SNN libraries driving developer accessibility.

Strategic business intelligence includes comprehensive competitive landscape analysis, funding and investment tracking, merger and acquisition activity, and partnership developments shaping industry dynamics. The report profiles 149 companies across the neuromorphic value chain, from semiconductor giants to innovative startups pioneering brain-inspired computing solutions.

Market drivers analyzed include the unsustainable energy trajectory of conventional AI, proliferating edge device deployments, autonomous vehicle development, and breakthrough achievements in commercial neuromorphic hardware. Challenges addressed encompass the programming paradigm gap, manufacturing scalability, software ecosystem fragmentation, and developer talent shortages, with resolution timelines projected through 2036.

The report provides technology roadmaps spanning near-term commercialization through long-term research horizons, enabling strategic planning for product development, investment timing, and market entry decisions. Comparative analysis positions neuromorphic computing against competing emerging technologies including quantum computing, photonic computing, and analog AI chips.

IDC projects neuromorphic technology could power 30% of edge AI devices by 2030, representing a fundamental transformation in artificial intelligence infrastructure. Applications spanning autonomous vehicles, humanoid robotics, brain-computer interfaces, cybersecurity, and energy-efficient data centres are driving adoption across industries. This report serves technology executives, venture capital investors, corporate strategists, semiconductor manufacturers, system integrators, and government policymakers requiring authoritative market intelligence on neuromorphic computing and sensing technologies. The analysis synthesizes primary research, company disclosures, patent analysis, and expert interviews to deliver the most comprehensive assessment of this transformative market available.

Report Contents Include:

  • Global market revenues and forecasts 2024-2036
  • Market segmentation by technology, application, and region
  • Key market trends, growth drivers, and challenges
  • Industry insights on digital vs. analog implementations
  • Technology roadmap and future outlook
  • Key product launches 2024-2025
  • Funding, investments, and M&A activity
  • Regulatory and ethical considerations
  • Sustainability and environmental impact analysis
  • Technology Deep-Dive
    • Spiking Neural Networks (SNNs) architecture and principles
    • Memory technologies: SRAM, DRAM, PCM, RRAM, MRAM, FeRAM
    • In-memory and near-memory computing approaches
    • Neuromorphic hardware: digital, analog, mixed-signal, FPGA-based processors
    • Software frameworks, programming tools, and SDKs
    • Algorithm libraries and simulation platforms
  • Neuromorphic Sensing Technologies
    • Event-based vision, auditory, and olfactory sensors
    • Hybrid sensing approaches and multi-modal fusion
    • Pixel-level processing and sensor-processor co-design
    • Signal processing and feature extraction techniques
    • Spike-based encoding and temporal feature extraction
  • Application Market Analysis & Forecasts
    • Mobile and consumer applications
    • Automotive and transportation (ADAS, autonomous vehicles)
    • Industrial IoT and smart manufacturing
    • Healthcare and medical devices
    • Aerospace and defense
    • Datacenters and cloud services
    • Commercial deployment case studies
  • Regional Market Analysis
    • North America market size and forecasts
    • Europe market dynamics and key initiatives
    • Asia-Pacific growth drivers and opportunities
    • Rest of World emerging markets
    • Regional development highlights and government initiatives
  • Competitive Landscape
    • Neuromorphic chip manufacturers
    • Sensor manufacturers
    • Emerging NVM manufacturers
    • Software and framework providers
    • Research institutions and academia
    • Competing emerging technologies analysis
    • Technology substitution and migration pathways
  • 151 Company Profiles
    • Business overview and product portfolios
    • Technology capabilities and roadmaps
    • Strategic partnerships and funding
    • Market positioning and competitive advantages

This report features detailed profiles of 151 leading companies shaping the neuromorphic computing and sensing industry: ABR (Applied Brain Research), AiM Future, AI Startek, AI Storm, AlpsenTek, Amazon Web Services (AWS), Ambarella, Ambient Scientific, Advanced Micro Devices (AMD), ANAFLASH, Analog Inference, AnotherBrain, Apple, ARM, Aryballe Technologies, Aspinity, Aspirare Semi, Avalanche Technology, Axelera AI, Baidu Inc., Beijing Xinzhida Neurotechnology, Blumind Inc., BMW, Bosch, BrainChip, Canon, CEA-Leti, Celepixel, Celestial AI, Cerebras Systems, Ceryx Medical, Ceva Inc., ChipIntelli, Clarifai, CoCoPIE, Cognifiber, Crossbar Inc., d-Matrix, DeepLite, DeepX, Dialog Semiconductor, Dynex, EdgeCortix, Eeasy Technology, Evomotion, Expedera, Fullhan, General Vision, GlobalFoundries, Google, Gorilla Technology, GrAI Matter Labs, Green Mountain Semiconductor, Grayscale AI, Groq, Gwanak Analog Co. Ltd., Hailo, HPLabs, Hikvision, Huawei, IBM, Infineon Technologies AG, iniVation AG, Innatera Nanosystems B.V., Instar-Robotics, Intel, Intelligent Hardware Korea (IHWK), Intrinsic Semiconductor Technologies, Kalray SA, KIST (Korea Institute of Science and Technology), Koniku, Kneron, Knowm, Lightmatter, Lumai, Lynxi Technology, MatX, MediaTek, MemComputing Inc., MemryX, Mentium Technologies, Meta, Microsoft, Mindtrace, Moffett AI, Mythic, MythWorx and more.....

TABLE OF CONTENTS

1 EXECUTIVE SUMMARY

  • 1.1 Overview of the neuromorphic computing and sensing market
    • 1.1.1 Market Performance 2024-2025
    • 1.1.2 Revised Long-Term Projections Through 2036
    • 1.1.3 Global Market Revenues 2024-2036
    • 1.1.4 Market segmentation
  • 1.2 Ending of Moore's Law
  • 1.3 Historical market
  • 1.4 The market in 2024
  • 1.5 Key market trends and growth drivers
  • 1.6 Market challenges and limitations
  • 1.7 Key Industry Insights
    • 1.7.1 Digital Neuromorphic Designs Replacing Analog Implementations
    • 1.7.2 The Programming Paradigm Gap
    • 1.7.3 Cost and Scalability Considerations
  • 1.8 Future outlook and opportunities
    • 1.8.1 Emerging trends
      • 1.8.1.1 Hybrid Neuromorphic-Conventional Computing and Sensing Systems
      • 1.8.1.2 Edge AI and IoT
      • 1.8.1.3 Quantum Computing
      • 1.8.1.4 Explainable AI
      • 1.8.1.5 Brain-Computer Interfaces
      • 1.8.1.6 Energy-efficient AI at scale
      • 1.8.1.7 Real-time learning and adaptation
      • 1.8.1.8 Enhanced Perception Systems
      • 1.8.1.9 Large-scale Neuroscience Simulations
      • 1.8.1.10 Secure, Decentralized AI
      • 1.8.1.11 Robotics that mimic humans
      • 1.8.1.12 Neural implants for healthcare
      • 1.8.1.13 New Application Areas and Use Cases
      • 1.8.1.14 Disruptive Business Models and Services
      • 1.8.1.15 Collaborative Ecosystem Development
      • 1.8.1.16 Skill Development and Workforce Training
    • 1.8.2 Technology roadmap
  • 1.9 Key Product Launches (2024-2025)
    • 1.9.1 Intel Hala Point System (April 2024)
    • 1.9.2 Innatera T1 SNP Processor (CES 2025)
    • 1.9.3 BrainChip Akida Pulsar
    • 1.9.4 BrainChip Akida Cloud (August 2025)
    • 1.9.5 SynSense Speck 2.0
    • 1.9.6 Chinese Academy of Sciences SpikingBrain-1.0
  • 1.10 Neuromorphic computing and generative AI
  • 1.11 Market value chain
  • 1.12 Market map
  • 1.13 Funding and investments
  • 1.14 Strategic Partnerships and Collaborations
  • 1.15 Regulatory and Ethical Considerations
    • 1.15.1 Data Privacy and Security
    • 1.15.2 Bias and Fairness in Neuromorphic Systems
    • 1.15.3 Intellectual Property and Patent Landscape
  • 1.16 Sustainability and Environmental Impact
    • 1.16.1 Carbon Footprint Analysis of Neuromorphic Systems
    • 1.16.2 Energy Efficiency Metrics and Benchmarking
    • 1.16.3 Green Manufacturing Practices
    • 1.16.4 End-of-life and Recycling Considerations
    • 1.16.5 Environmental Regulations Compliance

2 INTRODUCTION

  • 2.1 Definition and concept of neuromorphic computing and sensing
  • 2.2 Main neuromorphic approaches
    • 2.2.1 Large-scale hardware neuromorphic computing systems
    • 2.2.2 Non-volatile memory technologies
    • 2.2.3 Advanced memristive materials and devices
  • 2.3 Fabrication Processes for Neuromorphic Systems
  • 2.4 Key Material Suppliers
  • 2.5 Supply Chain Vulnerabilities and Mitigation
  • 2.6 Manufacturing Capacity Analysis
  • 2.7 Quality Control and Testing Procedures
  • 2.8 Comparison with traditional computing and sensing approaches
  • 2.9 Neuromorphic computing vs. quantum computing
  • 2.10 Key features and advantages
    • 2.10.1 Low latency and real-time processing
    • 2.10.2 Power efficiency and energy savings
    • 2.10.3 Scalability and adaptability
    • 2.10.4 Online learning and autonomous decision-making
  • 2.11 Markets and Applications
    • 2.11.1 Edge AI and IoT
    • 2.11.2 Autonomous Vehicles and Robotics
    • 2.11.3 Cybersecurity and Anomaly Detection
    • 2.11.4 Smart Sensors and Monitoring Systems
    • 2.11.5 Datacenter and High-Performance Computing

3 NEUROMORPHIC COMPUTING TECHNOLOGIES AND ARCHITECTURE

  • 3.1 Spiking Neural Networks (SNNs)
    • 3.1.1 Biological inspiration and principles
    • 3.1.2 Types of SNNs and their characteristics
    • 3.1.3 Advantages and limitations of SNNs
  • 3.2 Memory Architectures for Neuromorphic Computing
    • 3.2.1 Conventional memory approaches (SRAM, DRAM)
    • 3.2.2 Emerging non-volatile memory (eNVM) technologies
      • 3.2.2.1 Phase-Change Memory (PCM)
      • 3.2.2.2 Resistive RAM (RRAM)
      • 3.2.2.3 Magnetoresistive RAM (MRAM)
      • 3.2.2.4 Ferroelectric RAM (FeRAM)
    • 3.2.3 In-memory computing and near-memory computing
    • 3.2.4 Hybrid memory architectures
  • 3.3 Neuromorphic Hardware and Processors
    • 3.3.1 Digital neuromorphic processors
    • 3.3.2 Analog neuromorphic processors
    • 3.3.3 Mixed-signal neuromorphic processors
    • 3.3.4 FPGA-based neuromorphic systems
    • 3.3.5 Neuromorphic accelerators and co-processors
  • 3.4 Software and Frameworks for Neuromorphic Computing
    • 3.4.1 Neuromorphic programming languages and tools
    • 3.4.2 Neuromorphic simulation platforms and frameworks
    • 3.4.3 Software and Programming Ecosystem Developments
      • 3.4.3.1 Intel Lava Framework
      • 3.4.3.2 Neuromorphic Intermediate Representation (NIR)
      • 3.4.3.3 PyTorch-Based SNN Libraries
      • 3.4.3.4 Nengo Cross-Platform Framework
    • 3.4.4 Neuromorphic algorithm libraries and repositories
    • 3.4.5 Neuromorphic software development kits (SDKs)

4 NEUROMORPHIC SENSING TECHNOLOGIES AND ARCHITECTURES

  • 4.1 Event-Based Sensors and Processing
    • 4.1.1 Neuromorphic vision sensors
    • 4.1.2 Neuromorphic auditory sensors
    • 4.1.3 Neuromorphic olfactory sensors
    • 4.1.4 Event-driven processing and algorithms
  • 4.2 Hybrid Sensing Approaches
    • 4.2.1 Combination of conventional and event-based sensors
    • 4.2.2 Fusion of multiple sensing modalities
    • 4.2.3 Advantages and challenges of hybrid sensing
  • 4.3 Neuromorphic Sensor Architectures and Designs
    • 4.3.1 Pixel-level processing and computation
    • 4.3.2 Sensor-processor co-design and integration
    • 4.3.3 Bio-inspired sensor designs and materials
  • 4.4 Signal Processing and Feature Extraction Techniques
    • 4.4.1 Spike-based Encoding and Decoding
    • 4.4.2 Temporal and Spatiotemporal Feature Extraction
    • 4.4.3 Neuromorphic Filtering and Denoising
    • 4.4.4 Adaptive and Learning-Based Processing

5 MARKET ANALYSIS AND FORECASTS

  • 5.1 Commercial Deployment Highlights 2025
    • 5.1.1 IoT and Edge Deployments
    • 5.1.2 Automotive Applications: Mercedes-Benz
    • 5.1.3 Telecommunications: Ericsson Research
    • 5.1.4 Healthcare: ALYN Hospital Collaboration
    • 5.1.5 Cybersecurity Applications
  • 5.2 Mobile and Consumer Applications
    • 5.2.1 Smartphones and wearables
    • 5.2.2 Smart home and IoT devices
    • 5.2.3 Consumer health and wellness
    • 5.2.4 Entertainment and gaming
  • 5.3 Automotive and Transportation
    • 5.3.1 Advanced Driver Assistance Systems (ADAS)
    • 5.3.2 Autonomous vehicles and robotaxis
    • 5.3.3 Vehicle infotainment and user experience
    • 5.3.4 Smart traffic management and infrastructure
  • 5.4 Industrial and Manufacturing
    • 5.4.1 Industrial IoT and smart factories
    • 5.4.2 Predictive maintenance and anomaly detection
    • 5.4.3 Quality control and inspection
    • 5.4.4 Logistics and supply chain optimization
  • 5.5 Healthcare and Medical Devices
    • 5.5.1 Medical imaging and diagnostics
    • 5.5.2 Wearable health monitoring devices
    • 5.5.3 Personalized medicine and drug discovery
    • 5.5.4 Assistive technologies and prosthetics
  • 5.6 Aerospace and Defense
    • 5.6.1 Unmanned Aerial Vehicles (UAVs) and drones
    • 5.6.2 Satellite imaging and remote sensing
    • 5.6.3 Missile guidance and target recognition
    • 5.6.4 Cybersecurity and threat detection:
  • 5.7 Datacenters and Cloud Services
    • 5.7.1 High-performance computing and scientific simulations:
    • 5.7.2 Big data analytics and machine learning
    • 5.7.3 Cloud-based AI services and platforms
    • 5.7.4 Energy-efficient datacenter infrastructure
  • 5.8 Regional Market Analysis and Forecasts
    • 5.8.1 North America
    • 5.8.2 Europe
    • 5.8.3 Asia-Pacific
    • 5.8.4 Rest of the World
  • 5.9 Competitive Landscape and Key Players
    • 5.9.1 Overview of the Neuromorphic Computing and Sensing Ecosystem
    • 5.9.2 Neuromorphic Chip Manufacturers and Processors
    • 5.9.3 Neuromorphic Sensor Manufacturers
    • 5.9.4 Emerging Non-Volatile Memory (eNVM) Manufacturers
    • 5.9.5 Neuromorphic Software and Framework Providers
    • 5.9.6 Research Institutions and Academia
  • 5.10 Competing Emerging Technologies
    • 5.10.1 Quantum Computing
    • 5.10.2 Photonic Computing
    • 5.10.3 DNA Computing
    • 5.10.4 Spintronic Computing
    • 5.10.5 Chemical Computing
    • 5.10.6 Superconducting Computing
    • 5.10.7 Analog AI Chips
    • 5.10.8 In-Memory Computing
    • 5.10.9 Reversible Computing
    • 5.10.10 Quantum Dot Computing
    • 5.10.11 Technology Substitution Analysis
    • 5.10.12 Migration Pathways
    • 5.10.13 Comparative Advantages/Disadvantages

6 COMPANY PROFILES (151 company profiles)

7 RESEARCH SCOPE & METHODOLOGY

8 REFERENCES

List of Tables

  • Table 1. Overview of the neuromorphic computing and sensing market.
  • Table 2. Global market for neuromorphic computing and sensors, 2024-2036 (Billions USD).
  • Table 3. Neuromorphic Computing and Sensing Market Segmentation 2020-2036.
  • Table 4. Key market trends and growth drivers.
  • Table 5. Market challenges and limitations.
  • Table 6. Emerging Trends in Neuromorphic Computing and Sensing
  • Table 7. Key Product Launches.
  • Table 8. Neuromorphic computing and generative AI strategies.
  • Table 9. Funding and investments in neuromorphic computing and sensing.
  • Table 10. Strategic Partnerships and Collaborations in the Neuromorphic Industry.
  • Table 11. Regulatory and Ethical Considerations of neuromorphic computing & sensing.
  • Table 12. Main neuromorphic sensing approaches.
  • Table 13. Main Neuromorphic Computing Approaches.
  • Table 14. Resistive Non-Volatile Memory (NVM) Technologies.
  • Table 15. Advanced Memristive Materials, Devices, and Novel Computation Concepts.
  • Table 16. Fabrication Processes for Neuromorphic Systems.
  • Table 17. Key Material Suppliers and Dependencies.
  • Table 18. Comparison with traditional computing and sensing approaches.
  • Table 19. Comparison between neuromorphic and quantum computing.
  • Table 20. Key features and advantages of neuromorphic computing and sensing.
  • Table 21. Markets and Applications of Neuromorphic Computing and Sensing
  • Table 22. Von neumann architecture versus neuromorphic architecture.
  • Table 23. Types of SNNs and their characteristics.
  • Table 24. Advantages and limitations of SNNs.
  • Table 25. Conventional memory approaches (SRAM, DRAM).
  • Table 26. Emerging non-volatile memory (eNVM) technologies.
  • Table 27. Hybrid memory architectures.
  • Table 28. Neuromorphic accelerators and co-processors.
  • Table 29. Neuromorphic programming languages and tools.
  • Table 30. Neuromorphic simulation platforms and frameworks.
  • Table 31. Neuromorphic Software Ecosystem 2025-2036
  • Table 32. Software Ecosystem Maturity Timeline
  • Table 33. Neuromorphic algorithm libraries and repositories.
  • Table 34. Neuromorphic software development kits (SDKs).
  • Table 35. Hybrid sensing approaches.
  • Table 36. Advantages and challenges of hybrid sensing.
  • Table 37. Bio-inspired sensor designs and materials.
  • Table 38. Signal Processing and Feature Extraction Techniques.
  • Table 39. Applications of neuromorphic computing and sensing in smartphones and wearables-advantages, limitations and likelihood of market penetration by application.
  • Table 40. Applications of neuromorphic computing and sensing in smart homes and IoT devices- advantages, limitations and likelihood of market penetration by application.
  • Table 41. Applications of neuromorphic computing and sensing in Consumer Health and Wellness-- advantages, limitations and likelihood of market penetration by application.
  • Table 42. Applications of neuromorphic computing and sensing in Entertainment and Gaming-advantages, limitations and likelihood of market penetration by application.
  • Table 43. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Mobile and Consumer Applications (2024-2036), millions USD.
  • Table 44. Applications of neuromorphic computing and sensing in Advanced Driver Assistance Systems (ADAS) -advantages, limitations and likelihood of market penetration by application.
  • Table 45. Applications of neuromorphic computing and sensing in Autonomous Vehicles and Robotaxis-advantages, limitations and likelihood of market penetration by application.
  • Table 46. Applications of neuromorphic computing and sensing in Vehicle infotainment and user experience-advantages, limitations and likelihood of market penetration by application.
  • Table 47. Applications of neuromorphic computing and sensing in Vehicle infotainment and user experience-advantages, limitations and likelihood of market penetration by application.
  • Table 48. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Automotive and Transportation (2024-2036), millions USD.
  • Table 49. Applications of neuromorphic computing and sensing in Industrial IoT and smart factories-advantages, limitations and likelihood of market penetration by application.
  • Table 50. Applications of neuromorphic computing and sensing in Industrial IoT and smart factories-advantages, limitations and likelihood of market penetration by application.
  • Table 51. Applications of neuromorphic computing and sensing in Quality control and inspection-advantages, limitations and likelihood of market penetration by application.
  • Table 52. Applications of neuromorphic computing and sensing in Logistics and supply chain optimization-advantages, limitations and likelihood of market penetration by application.
  • Table 53. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Industrial and Manufacturing (2024-2036), millions USD.
  • Table 54. Applications of neuromorphic computing and sensing in medical imaging and diagnostics-advantages, limitations and likelihood of market penetration by application.
  • Table 55. Applications of neuromorphic computing and sensing in Wearable health monitoring devices-advantages, limitations and likelihood of market penetration by application.
  • Table 56. Applications of neuromorphic computing and sensing in Personalized medicine and drug discovery-advantages, limitations and likelihood of market penetration by application.
  • Table 57. Applications of neuromorphic computing and sensing in Assistive technologies and prosthetics -advantages, limitations and likelihood of market penetration by application.
  • Table 58. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Healthcare and Medical Devices (2024-2036), millions USD.
  • Table 59. Applications of neuromorphic computing and sensing in Unmanned Aerial Vehicles (UAVs) and drones-advantages, limitations and likelihood of market penetration by application.
  • Table 60. Applications of neuromorphic computing and sensing in Satellite imaging and remote sensing:-advantages, limitations and likelihood of market penetration by application.
  • Table 61. Applications of neuromorphic computing and sensing in Missile guidance and target recognition -advantages, limitations and likelihood of market penetration by application.
  • Table 62. Applications of neuromorphic computing and sensing in Cybersecurity and threat detection -advantages, limitations and likelihood of market penetration by application.
  • Table 63. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Aerospace and Defence (2024-2036), millions USD.
  • Table 64. Applications of neuromorphic computing and sensing in High-performance computing and scientific simulations-advantages, limitations and likelihood of market penetration by application.
  • Table 65. Applications of neuromorphic computing and sensing in Big data analytics and machine learning-advantages, limitations and likelihood of market penetration by application.
  • Table 66. Applications of neuromorphic computing and sensing in Cloud-based AI services and platforms -advantages, limitations and likelihood of market penetration by application.
  • Table 67. Applications of neuromorphic computing and sensing in Energy-efficient datacenter infrastructure-advantages, limitations and likelihood of market penetration by application.
  • Table 68. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Datacenters and Cloud Services (2024-2036), millions USD.
  • Table 69. Market revenues for neuromorphic computing and sensing by region from 2024-2036 in millions USD.
  • Table 70. Neuromorphic Computing and Sensing Market Size and Forecast, in North America (2024-2036), millions USD.
  • Table 71. Neuromorphic Computing and Sensing Market Size and Forecast, in Europe (2024-2036), millions USD.
  • Table 72. Neuromorphic Computing and Sensing Market Size and Forecast, in Asia-Pacific (2024-2036), millions USD.
  • Table 73. Neuromorphic Computing and Sensing Market Size and Forecast, in the Rest of the World (2024-2036), millions USD.
  • Table 74. Neuromorphic Chip Manufacturers and Their Product Offerings.
  • Table 75. Neuromorphic Sensor Manufacturers and Their Product Offerings.
  • Table 76. Emerging Non-Volatile Memory (eNVM) Manufacturers and Their Product Offerings.
  • Table 77. Neuromorphic Software and Framework Providers and Their Solutions.
  • Table 78. Key Research Institutions and Academia in Neuromorphic Computing and Sensing.
  • Table 79. Competing Emerging Technologies for Neuromorphic Computing and Sensing.
  • Table 80. Technology Substitution Analysis.
  • Table 81. Comparative Advantages/Disadvantages.
  • Table 82. Evolution of Apple Neural Engine.
  • Table 83. Dynex subscription plans.

List of Figures

  • Figure 1. Global market for neuromorphic computing and sensors, 2023-2036 (Millions USD).
  • Figure 2. Neuromorphic computing and sensing technology roadmap.
  • Figure 3. Market value chain for neuromorphic computing and sensing.
  • Figure 4. Neuromorphic computing and sensing market map.
  • Figure 5. Evolution of the main hardware technologies for neuromorphic computing.
  • Figure 6. Key materials in NVM technology for neuromorphic computing.
  • Figure 7. Advanced memristive materials for neuromorphic computing.
  • Figure 8. Neural networks in autonomous vehicles.
  • Figure 9. Concept illustration of centralized and decentralized intelligence in robotics.
  • Figure 10. Neuromorphic programmable robot with dynamic vision developed by SynSense.
  • Figure 11. Comparison of High-Level Conventional and Neuromorphic Memory Architectures.
  • Figure 12. Spiking Neural Network (SNN) Structure and Operation.
  • Figure 13. IBM TrueNorth Processor.
  • Figure 14. Event-Based Sensor Operation and Data Processing Flow.
  • Figure 15. Conventional sensor vs. Event-based sensor.
  • Figure 16. Operation of neuromorphic vision sensors.
  • Figure 17. Cyranose 320 Electronic Nose.
  • Figure 18. Alpix-Pilatus platform, an integrated event-based vision sensor that combines static and dynamic information.
  • Figure 19. Commercial Application Milestones 2024-2025
  • Figure 20. Application Market Penetration Timeline
  • Figure 21. Technology roadmap for neuromorphic computing and sensing in mobile and consumer applications.
  • Figure 22. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Mobile and Consumer Applications (2024-2036), millions USD.
  • Figure 23. Technology Roadmap for Neuromorphic Computing and Sensing in Automotive and Transportation.
  • Figure 24. Sensors used by the ADAS (Advanced Driver-Assistance System).
  • Figure 25. Enabling technologies for autonomous vehicles.
  • Figure 26. Autonomous Vehicle Architecture with Neuromorphic Computing and Sensing.
  • Figure 27. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Automotive and Transportation (2024-2036), millions USD.
  • Figure 28. Technology roadmap for neuromorphic computing and sensing in industrial and manufacturing.
  • Figure 29. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Industrial and Manufacturing (2024-2036), millions USD.
  • Figure 30. Technology roadmap for neuromorphic computing and sensing in healthcare and medical devices.
  • Figure 31. Wearable Medical Devices with Neuromorphic Computing and Sensing Capabilities.
  • Figure 32. Flexible neuromorphic electronics for neuromorphic computing, humanoid robotics, and neuroprosthetics.
  • Figure 33. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Healthcare and Medical Devices (2024-2036), millions USD.
  • Figure 34. Technology roadmap for neuromorphic computing and sensing in aerospace and defense.
  • Figure 35. Schematic route from bio-inspired behaviours toward neuromorphic sensors for autonomous flight.
  • Figure 36. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Aerospace and Defence (2024-2036), millions USD.
  • Figure 37. Technology roadmap for neuromorphic computing and sensing in Datacenters and Cloud Services.
  • Figure 38. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Datacenters and Cloud Services (2024-2036), millions USD.
  • Figure 39. Global Neuromorphic Computing and Sensing Market Size and Forecast, in North America (2024-2036), millions USD.
  • Figure 40. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Europe (2024-2036), millions USD.
  • Figure 41. Global Neuromorphic Computing and Sensing Market Size and Forecast, Asia-Pacific (2024-2036), millions USD.
  • Figure 42. Global Neuromorphic Computing and Sensing Market Size and Forecast, in in the Rest of the World (2024-2036), millions USD.
  • Figure 43. Neuromorphic Computing and Sensing Ecosystem Overview.
  • Figure 44. Cerebas WSE-2.
  • Figure 45. DeepX NPU DX-GEN1.
  • Figure 46. Google TPU.
  • Figure 47. GrAI VIP.
  • Figure 48. Groq Tensor Streaming Processor (TSP).
  • Figure 49. DVL-5000 neuromorphic laser profiler.
  • Figure 50. Spiking Neural Processor
  • Figure 51. TROOPER robot.
  • Figure 52. 11th Gen Intel-R Core-TM S-Series.
  • Figure 53. Intel Loihi 2 chip.
  • Figure 54. Envise.
  • Figure 55. Pentonic 2000.
  • Figure 56. Azure Maia 100 and Cobalt 100 chips.
  • Figure 57. Mythic MP10304 Quad-AMP PCIe Card.
  • Figure 58. Nvidia H200 AI chip.
  • Figure 59. Grace Hopper Superchip.
  • Figure 60. Prophesee Metavision starter kit - AMD Kria KV260 and active marker LED board.
  • Figure 61. Cloud AI 100.
  • Figure 62. Overview of SpiNNaker2 architecture for the "SpiNNcloud" cloud system and edge systems.
  • Figure 63. Untether AI chip.