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市場調查報告書
商品編碼
1935824

全球神經形態計算與感測市場（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 | 訂單完成後即時交付

價格

全球神經型態運算和感測市場是半導體發展領域最具變革性的新興領域之一，與傳統數位運算和量子運算並駕齊驅，成為「第三大趨勢」。這項受大腦啟發的技術採用與傳統馮·諾依曼架構截然不同的架構來處理訊息，透過將記憶體和處理單元置於同一位置，消除了傳統CPU和GPU性能受限的、耗能巨大的往返資料傳輸。國際能源總署（IEA）預測，到2030年，資料中心的電力消耗的3%，這主要是由於神經網路模擬所需的運算資源。神經型態計算透過在硬體上實現神經網路，而非透過二元序列定序，直接應對了這項永續性挑戰。英特爾的神經型態處理器Loihi 2在某些推理任務中展現出比傳統處理器高出100倍的能效，而BrainChip的Akida Pulsar的能耗則比傳統AI核心降低了500倍。

競爭格局呈現多元化的生態系統，既有成熟的科技巨頭，也有創新Start-Ups。英特爾的Hala Point系統將於2024年在桑迪亞國家實驗室部署，它是全球最大的神經型態平台，擁有1152個Loihi 2處理器和11.5億個神經元。 IBM的基礎技術TrueNorth正透過神經突觸研究不斷發展，而BrainChip已成功將其Akida處理器商業化，應用於全球數百萬台物聯網設備。歐洲企業正透過英國多學科神經形態運算中心等專案加速發展，而包括SynSense和華為在內的中國企業則在物聯網和智慧城市領域大力推動應用。

推動神經形態晶片普及的關鍵應用領域包括邊緣人工智慧和物聯網。神經型態晶片能夠使智慧感測器、無人機和自動駕駛汽車以極低的電力消耗進行即時決策。在醫療應用領域，例如攜帶式診斷設備、可檢測心率異常的穿戴式監測器以及能夠實現人機無縫通訊的腦機介面。網路安全領域預計將即時商業性應用，因為神經型態系統擅長偵測網路流量中的細微異常。在金融服務領域，神經形態晶片被用於分析高頻交易和檢測複雜資料流中的詐欺行為；而在工業應用領域，則包括預測性維護、品質檢測和供應鏈最佳化。

儘管市場前景可期，但仍面臨諸多挑戰，例如可擴展性限制、與現有基礎設施整合的複雜性以及對標準化程式框架的需求。與傳統運算相比，軟體生態系統仍不發達，開發針對神經型態硬體最佳化的演算法需要一種全新的方法。然而，隨著數位神經型態設計（作為模擬實現的替代方案）的進步以及諸如神經形態中間表示（NIR）等標準化工作的推進，這些障礙正逐步被克服。

人工智慧工作負載的爆炸性成長、邊緣設備的激增以及對能源永續性日益成長的需求正在匯聚，使神經型態運算正處於關鍵的轉折點。隨著這項技術從實驗室走向商業產品，其實現更智慧、更適應、更節能的運算的潛力表明，神經型態系統將在2035年後不斷發展的人工智慧領域中扮演越來越重要的角色。

本報告對全球神經形態計算和感測市場進行了全面分析，並按技術類型、應用領域和地區提供了詳細的市場預測。

目錄

第1章執行摘要

  • 神經型態計算與感測市場概覽
  • 莫耳定律的終結
  • 市場表現
  • 市場（2024）
  • 主要市場趨勢和成長促進因素
  • 市場挑戰與局限性
  • 關鍵業界考察
  • 未來展望與機遇
  • 主要產品發布計畫（2024-2025）
  • 神經型態運算與生成式人工智慧
  • 市場價值鏈
  • 市場地圖
  • 資金籌措及投資
  • 策略夥伴關係和聯盟
  • 監管和倫理方面的考量
  • 永續性和環境影響

第2章：引言

  • 神經型態計算與感知的定義與概念
  • 主要神經型態方法
  • 神經型態系統的製造程序
  • 主要材料供應商
  • 供應鏈脆弱性及因應措施
  • 生產能力分析
  • 品管和測試方法
  • 與傳統計算和感測方法的比較
  • 神經型態計算與量子計算的比較
  • 主要特點和優勢
  • 市場與應用

第3章：神經型態運算的技術與架構

  • 脈衝神經網路（SNN）
  • 神經型態運算的記憶體架構
  • 神經型態硬體處理器
  • 用於神經型態計算的軟體框架

第4章：神經型態技術與架構

  • 基於事件的感測器和處理
  • 混合感測方法
  • 神經型態感測器架構與設計
  • 訊號處理和特徵提取技術

第5章 市場分析與預測

  • 商業發展亮點（2025 年）
  • 行動和消費者使用
  • 汽車和交通運輸
  • 工業和製造業
  • 醫療及醫療設備
  • 航太/國防
  • 資料中心雲端服務
  • 市場分析與預測：按地區分類
  • 競爭格局和主要企業
  • 相互競爭的新技術

第6章：公司簡介（151家公司簡介）

第7章：調查方法

第8章參考文獻

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.