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Neuromorphic Computing & Sensing Market

The Global Neuromorphic Computing & Sensing Market 2026-2036

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The global neuromorphic computing market report 2026-2036 from Future Markets provides authoritative analysis of brain-inspired computing hardware, spiking neural network architectures, neuromorphic sensors, and their applications in edge AI, robotics, autonomous systems, and scientific computing. While conventional deep learning dominates AI workloads today, neuromorphic approaches offer compelling energy efficiency and temporal processing advantages for specific problem classes — particularly in always-on sensing, event-driven processing, and robotic motor control.

Neuromorphic Computing & Sensing Market Report 2026-2036 — Key Coverage Areas

  • Neuromorphic Chip Architectures — Intel Loihi, IBM TrueNorth, BrainScaleS, SpiNNaker, and next-generation spiking neural network processors: architecture comparisons and performance benchmarks
  • Event-Based Vision Sensors — dynamic vision sensors (DVS), event cameras, and their applications in high-speed robotics, autonomous driving, and industrial inspection
  • Spiking Neural Networks — training methodologies, inference performance, energy consumption advantages, and application-specific benchmarking vs conventional neural networks
  • Edge AI Applications — always-on keyword detection, gesture recognition, biosignal monitoring, and sensor fusion in ultra-low-power edge devices
  • Robotics & Motor Control — neuromorphic approaches to sensorimotor integration, proprioception, and adaptive locomotion
  • Research & Development Landscape — academic centres, government programmes, and company R&D in neuromorphic computing globally
  • 10-Year Forecasts — chip shipments, market value, and application revenue by segment and region through 2036

Ideal for semiconductor investors, AI hardware developers, robotics engineers, and edge computing product teams.

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  • Published: April 2026
  • Pages: 367
  • Tables: 83
  • Figures: 63

 

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 100× over conventional processors for certain inference tasks, while BrainChip's Akida Pulsar delivers 500× 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.

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.

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.....

 

 

 

 

1             EXECUTIVE SUMMARY            18

  • 1.1        Overview of the neuromorphic computing and sensing market   18
    • 1.1.1    Market Performance 2024-2025       19
    • 1.1.2    Revised Long-Term Projections Through 2036         19
    • 1.1.3    Global Market Revenues 2024-2036              20
    • 1.1.4    Market segmentation               21
  • 1.2        Ending of Moore’s Law             22
  • 1.3        Historical market         23
  • 1.4        The market in 2024    24
  • 1.5        Key market trends and growth drivers            24
  • 1.6        Market challenges and limitations   25
  • 1.7        Key Industry Insights 26
    • 1.7.1    Digital Neuromorphic Designs Replacing Analog Implementations          26
    • 1.7.2    The Programming Paradigm Gap      27
    • 1.7.3    Cost and Scalability Considerations              27
  • 1.8        Future outlook and opportunities     28
    • 1.8.1    Emerging trends           28
      • 1.8.1.1 Hybrid Neuromorphic-Conventional Computing and Sensing Systems 29
      • 1.8.1.2 Edge AI and IoT             30
      • 1.8.1.3 Quantum Computing               31
      • 1.8.1.4 Explainable AI                31
      • 1.8.1.5 Brain-Computer Interfaces   32
      • 1.8.1.6 Energy-efficient AI at scale   32
      • 1.8.1.7 Real-time learning and adaptation  33
      • 1.8.1.8 Enhanced Perception Systems          33
      • 1.8.1.9 Large-scale Neuroscience Simulations       34
      • 1.8.1.10            Secure, Decentralized AI        34
      • 1.8.1.11            Robotics that mimic humans             34
      • 1.8.1.12            Neural implants for healthcare          35
      • 1.8.1.13            New Application Areas and Use Cases        35
      • 1.8.1.14            Disruptive Business Models and Services  36
      • 1.8.1.15            Collaborative Ecosystem Development       36
      • 1.8.1.16            Skill Development and Workforce Training 36
    • 1.8.2    Technology roadmap 38
  • 1.9        Key Product Launches (2024-2025) 39
    • 1.9.1    Intel Hala Point System (April 2024)               39
    • 1.9.2    Innatera T1 SNP Processor (CES 2025)        40
    • 1.9.3    BrainChip Akida Pulsar            40
    • 1.9.4    BrainChip Akida Cloud (August 2025)           40
    • 1.9.5    SynSense Speck 2.0 40
    • 1.9.6    Chinese Academy of Sciences SpikingBrain-1.0   40
  • 1.10     Neuromorphic computing and generative AI             41
  • 1.11     Market value chain     42
  • 1.12     Market map    45
  • 1.13     Funding and investments      47
  • 1.14     Strategic Partnerships and Collaborations 50
  • 1.15     Regulatory and Ethical Considerations        55
    • 1.15.1 Data Privacy and Security      55
    • 1.15.2 Bias and Fairness in Neuromorphic Systems           55
    • 1.15.3 Intellectual Property and Patent Landscape             55
  • 1.16     Sustainability and Environmental Impact   56
    • 1.16.1 Carbon Footprint Analysis of Neuromorphic Systems        56
    • 1.16.2 Energy Efficiency Metrics and Benchmarking          57
    • 1.16.3 Green Manufacturing Practices         57
    • 1.16.4 End-of-life and Recycling Considerations  57
    • 1.16.5 Environmental Regulations Compliance     57

 

2             INTRODUCTION          59

  • 2.1        Definition and concept of neuromorphic computing and sensing              59
  • 2.2        Main neuromorphic approaches      60
    • 2.2.1    Large-scale hardware neuromorphic computing systems               62
    • 2.2.2    Non-volatile memory technologies 62
    • 2.2.3    Advanced memristive materials and devices           63
  • 2.3        Fabrication Processes for Neuromorphic Systems               64
  • 2.4        Key Material Suppliers             64
  • 2.5        Supply Chain Vulnerabilities and Mitigation              65
  • 2.6        Manufacturing Capacity Analysis    65
  • 2.7        Quality Control and Testing Procedures       66
  • 2.8        Comparison with traditional computing and sensing approaches             66
  • 2.9        Neuromorphic computing vs. quantum computing             67
  • 2.10     Key features and advantages              68
    • 2.10.1 Low latency and real-time processing          68
    • 2.10.2 Power efficiency and energy savings              68
    • 2.10.3 Scalability and adaptability 69
    • 2.10.4 Online learning and autonomous decision-making             69
  • 2.11     Markets and Applications      69
    • 2.11.1 Edge AI and IoT             73
    • 2.11.2 Autonomous Vehicles and Robotics              74
    • 2.11.3 Cybersecurity and Anomaly Detection         76
    • 2.11.4 Smart Sensors and Monitoring Systems      77
    • 2.11.5 Datacenter and High-Performance Computing      78

 

3             NEUROMORPHIC COMPUTING TECHNOLOGIES AND ARCHITECTURE               80

  • 3.1        Spiking Neural Networks (SNNs)      81
    • 3.1.1    Biological inspiration and principles              82
    • 3.1.2    Types of SNNs and their characteristics      82
    • 3.1.3    Advantages and limitations of SNNs              82
  • 3.2        Memory Architectures for Neuromorphic Computing        83
    • 3.2.1    Conventional memory approaches (SRAM, DRAM)              83
    • 3.2.2    Emerging non-volatile memory (eNVM) technologies         84
      • 3.2.2.1 Phase-Change Memory (PCM)           84
      • 3.2.2.2 Resistive RAM (RRAM)             84
      • 3.2.2.3 Magnetoresistive RAM (MRAM)          85
      • 3.2.2.4 Ferroelectric RAM (FeRAM)   85
    • 3.2.3    In-memory computing and near-memory computing         86
    • 3.2.4    Hybrid memory architectures             86
  • 3.3        Neuromorphic Hardware and Processors  87
    • 3.3.1    Digital neuromorphic processors     87
    • 3.3.2    Analog neuromorphic processors    88
    • 3.3.3    Mixed-signal neuromorphic processors       88
    • 3.3.4    FPGA-based neuromorphic systems             89
    • 3.3.5    Neuromorphic accelerators and co-processors     89
  • 3.4        Software and Frameworks for Neuromorphic Computing               90
    • 3.4.1    Neuromorphic programming languages and tools               90
    • 3.4.2    Neuromorphic simulation platforms and frameworks       91
    • 3.4.3    Software and Programming Ecosystem Developments     92
      • 3.4.3.1 Intel Lava Framework               92
      • 3.4.3.2 Neuromorphic Intermediate Representation (NIR)               92
      • 3.4.3.3 PyTorch-Based SNN Libraries             92
      • 3.4.3.4 Nengo Cross-Platform Framework  93
    • 3.4.4    Neuromorphic algorithm libraries and repositories              93
    • 3.4.5    Neuromorphic software development kits (SDKs) 94

 

4             NEUROMORPHIC SENSING TECHNOLOGIES AND ARCHITECTURES     96

  • 4.1        Event-Based Sensors and Processing           96
    • 4.1.1    Neuromorphic vision sensors             97
    • 4.1.2    Neuromorphic auditory sensors       98
    • 4.1.3    Neuromorphic olfactory sensors      99
    • 4.1.4    Event-driven processing and algorithms     100
  • 4.2        Hybrid Sensing Approaches 100
    • 4.2.1    Combination of conventional and event-based sensors   102
    • 4.2.2    Fusion of multiple sensing modalities          102
    • 4.2.3    Advantages and challenges of hybrid sensing         103
  • 4.3        Neuromorphic Sensor Architectures and Designs                104
    • 4.3.1    Pixel-level processing and computation      104
    • 4.3.2    Sensor-processor co-design and integration            104
    • 4.3.3    Bio-inspired sensor designs and materials                105
  • 4.4        Signal Processing and Feature Extraction Techniques        106
    • 4.4.1    Spike-based Encoding and Decoding            107
    • 4.4.2    Temporal and Spatiotemporal Feature Extraction 107
    • 4.4.3    Neuromorphic Filtering and Denoising         108
    • 4.4.4    Adaptive and Learning-Based Processing  109

 

5             MARKET ANALYSIS AND FORECASTS             111

  • 5.1        Commercial Deployment Highlights 2025 111
    • 5.1.1    IoT and Edge Deployments    111
    • 5.1.2    Automotive Applications: Mercedes-Benz  111
    • 5.1.3    Telecommunications: Ericsson Research   111
    • 5.1.4    Healthcare: ALYN Hospital Collaboration  111
    • 5.1.5    Cybersecurity Applications  112
  • 5.2        Mobile and Consumer Applications               113
    • 5.2.1    Smartphones and wearables              113
    • 5.2.2    Smart home and IoT devices                114
    • 5.2.3    Consumer health and wellness         115
    • 5.2.4    Entertainment and gaming   116
  • 5.3        Automotive and Transportation         119
    • 5.3.1    Advanced Driver Assistance Systems (ADAS)          119
    • 5.3.2    Autonomous vehicles and robotaxis              121
    • 5.3.3    Vehicle infotainment and user experience  124
    • 5.3.4    Smart traffic management and infrastructure          125
  • 5.4        Industrial and Manufacturing             128
    • 5.4.1    Industrial IoT and smart factories     129
    • 5.4.2    Predictive maintenance and anomaly detection    130
    • 5.4.3    Quality control and inspection           131
    • 5.4.4    Logistics and supply chain optimization     132
  • 5.5        Healthcare and Medical Devices      135
    • 5.5.1    Medical imaging and diagnostics     136
    • 5.5.2    Wearable health monitoring devices              137
    • 5.5.3    Personalized medicine and drug discovery                138
    • 5.5.4    Assistive technologies and prosthetics        140
  • 5.6        Aerospace and Defense         143
    • 5.6.1    Unmanned Aerial Vehicles (UAVs) and drones        144
    • 5.6.2    Satellite imaging and remote sensing            145
    • 5.6.3    Missile guidance and target recognition      146
    • 5.6.4    Cybersecurity and threat detection:               148
  • 5.7        Datacenters and Cloud Services      151
    • 5.7.1    High-performance computing and scientific simulations:              151
    • 5.7.2    Big data analytics and machine learning     153
    • 5.7.3    Cloud-based AI services and platforms       154
    • 5.7.4    Energy-efficient datacenter infrastructure 155
  • 5.8        Regional Market Analysis and Forecasts     158
    • 5.8.1    North America              159
    • 5.8.2    Europe                160
    • 5.8.3    Asia-Pacific    162
    • 5.8.4    Rest of the World         163
  • 5.9        Competitive Landscape and Key Players     165
    • 5.9.1    Overview of the Neuromorphic Computing and Sensing Ecosystem        165
    • 5.9.2    Neuromorphic Chip Manufacturers and Processors           165
    • 5.9.3    Neuromorphic Sensor Manufacturers          166
    • 5.9.4    Emerging Non-Volatile Memory (eNVM) Manufacturers    167
    • 5.9.5    Neuromorphic Software and Framework Providers              167
    • 5.9.6    Research Institutions and Academia             168
  • 5.10     Competing Emerging Technologies 171
    • 5.10.1 Quantum Computing               171
    • 5.10.2 Photonic Computing 172
    • 5.10.3 DNA Computing          172
    • 5.10.4 Spintronic Computing             172
    • 5.10.5 Chemical Computing               172
    • 5.10.6 Superconducting Computing             173
    • 5.10.7 Analog AI Chips            173
    • 5.10.8 In-Memory Computing            173
    • 5.10.9 Reversible Computing             173
    • 5.10.10              Quantum Dot Computing      174
    • 5.10.11              Technology Substitution Analysis    174
    • 5.10.12              Migration Pathways    176
    • 5.10.13              Comparative Advantages/Disadvantages  177

 

6             COMPANY PROFILES                178 (151 company profiles)

 

7             RESEARCH SCOPE & METHODOLOGY        361

 

8             REFERENCES 362

 

List of Tables

  • Table 1. Overview of the neuromorphic computing and sensing market.               18
  • Table 2. Global market for neuromorphic computing and sensors, 2024-2036 (Billions USD).               20
  • Table 3. Neuromorphic Computing and Sensing Market Segmentation 2020-2036.      21
  • Table 4. Key market trends and growth drivers.       25
  • Table 5. Market challenges and limitations.              26
  • Table 6. Emerging Trends in Neuromorphic Computing and Sensing        28
  • Table 7. Key Product Launches.        41
  • Table 8. Neuromorphic computing and generative AI strategies. 42
  • Table 9. Funding and investments in neuromorphic computing and sensing.    47
  • Table 10. Strategic Partnerships and Collaborations in the Neuromorphic Industry.     50
  • Table 11. Regulatory and Ethical Considerations of neuromorphic computing & sensing.        56
  • Table 12. Main neuromorphic sensing approaches.            60
  • Table 13. Main Neuromorphic Computing Approaches.   60
  • Table 14. Resistive Non-Volatile Memory (NVM) Technologies.    62
  • Table 15. Advanced Memristive Materials, Devices, and Novel Computation Concepts.            63
  • Table 16. Fabrication Processes for Neuromorphic Systems.       64
  • Table 17. Key Material Suppliers and Dependencies.          64
  • Table 18. Comparison with traditional computing and sensing approaches.      66
  • Table 19. Comparison between neuromorphic and quantum computing.            67
  • Table 20. Key features and advantages of neuromorphic computing and sensing.         68
  • Table 21. Markets and Applications of Neuromorphic Computing and Sensing 70
  • Table 22. Von neumann architecture versus neuromorphic architecture.             80
  • Table 23. Types of SNNs and their characteristics.               82
  • Table 24. Advantages and limitations of SNNs.       83
  • Table 25. Conventional memory approaches (SRAM, DRAM).      83
  • Table 26. Emerging non-volatile memory (eNVM) technologies.  84
  • Table 27. Hybrid memory architectures.      87
  • Table 28. Neuromorphic accelerators and co-processors.             90
  • Table 29. Neuromorphic programming languages and tools.        91
  • Table 30. Neuromorphic simulation platforms and frameworks. 91
  • Table 31. Neuromorphic Software Ecosystem 2025-2036              93
  • Table 32. Software Ecosystem Maturity Timeline   93
  • Table 33. Neuromorphic algorithm libraries and repositories.      94
  • Table 34. Neuromorphic software development kits (SDKs).         95
  • Table 35. Hybrid sensing approaches.          101
  • Table 36. Advantages and challenges of hybrid sensing.  103
  • Table 37. Bio-inspired sensor designs and materials.         105
  • Table 38. Signal Processing and Feature Extraction Techniques. 106
  • Table 39. Applications of neuromorphic computing and sensing in smartphones and wearables-advantages, limitations and likelihood of market penetration by application.    114
  • Table 40. Applications of neuromorphic computing and sensing in smart homes and IoT devices- advantages, limitations and likelihood of market penetration by application.    114
  • Table 41. Applications of neuromorphic computing and sensing in Consumer Health and Wellness-- advantages, limitations and likelihood of market penetration by application.    115
  • Table 42. Applications of neuromorphic computing and sensing in Entertainment and Gaming-advantages, limitations and likelihood of market penetration by application.    117
  • Table 43. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Mobile and Consumer Applications (2024-2036), millions USD.           117
  • Table 44. Applications of neuromorphic computing and sensing in Advanced Driver Assistance Systems (ADAS) -advantages, limitations and likelihood of market penetration by application. 120
  • Table 45. Applications of neuromorphic computing and sensing in Autonomous Vehicles and Robotaxis-advantages, limitations and likelihood of market penetration by application.           123
  • Table 46. Applications of neuromorphic computing and sensing in Vehicle infotainment and user experience-advantages, limitations and likelihood of market penetration by application.        125
  • Table 47. Applications of neuromorphic computing and sensing in Vehicle infotainment and user experience-advantages, limitations and likelihood of market penetration by application.        126
  • Table 48. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Automotive and Transportation (2024-2036), millions USD. 126
  • Table 49. Applications of neuromorphic computing and sensing in Industrial IoT and smart factories-advantages, limitations and likelihood of market penetration by application.    129
  • Table 50. Applications of neuromorphic computing and sensing in Industrial IoT and smart factories-advantages, limitations and likelihood of market penetration by application.    131
  • Table 51. Applications of neuromorphic computing and sensing in Quality control and inspection-advantages, limitations and likelihood of market penetration by application.    132
  • Table 52. Applications of neuromorphic computing and sensing in Logistics and supply chain optimization-advantages, limitations and likelihood of market penetration by application.     133
  • Table 53. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Industrial and Manufacturing (2024-2036), millions USD. 134
  • Table 54. Applications of neuromorphic computing and sensing in medical imaging and diagnostics-advantages, limitations and likelihood of market penetration by application.    136
  • Table 55. Applications of neuromorphic computing and sensing in Wearable health monitoring devices-advantages, limitations and likelihood of market penetration by application.    138
  • Table 56. Applications of neuromorphic computing and sensing in Personalized medicine and drug discovery-advantages, limitations and likelihood of market penetration by application.            139
  • Table 57. Applications of neuromorphic computing and sensing in Assistive technologies and prosthetics -advantages, limitations and likelihood of market penetration by application.       141
  • Table 58. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Healthcare and Medical Devices (2024-2036), millions USD.           141
  • Table 59. Applications of neuromorphic computing and sensing in Unmanned Aerial Vehicles (UAVs) and drones-advantages, limitations and likelihood of market penetration by application.        144
  • Table 60. Applications of neuromorphic computing and sensing in Satellite imaging and remote sensing:-advantages, limitations and likelihood of market penetration by application.               146
  • Table 61. Applications of neuromorphic computing and sensing in Missile guidance and target recognition -advantages, limitations and likelihood of market penetration by application.       147
  • Table 62. Applications of neuromorphic computing and sensing in Cybersecurity and threat detection -advantages, limitations and likelihood of market penetration by application.    148
  • Table 63. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Aerospace and Defence (2024-2036), millions USD.              149
  • Table 64. Applications of neuromorphic computing and sensing in High-performance computing and scientific simulations-advantages, limitations and likelihood of market penetration by application. 152
  • Table 65. Applications of neuromorphic computing and sensing in Big data analytics and machine learning-advantages, limitations and likelihood of market penetration by application.               153
  • Table 66. Applications of neuromorphic computing and sensing in Cloud-based AI services and platforms -advantages, limitations and likelihood of market penetration by application.          154
  • Table 67. Applications of neuromorphic computing and sensing in Energy-efficient datacenter infrastructure-advantages, limitations and likelihood of market penetration by application.  156
  • Table 68. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Datacenters and Cloud Services (2024-2036), millions USD.              157
  • Table 69. Market revenues for neuromorphic computing and sensing by region from 2024-2036 in millions USD. 158
  • Table 70. Neuromorphic Computing and Sensing Market Size and Forecast, in North America (2024-2036), millions USD. 159
  • Table 71. Neuromorphic Computing and Sensing Market Size and Forecast, in Europe (2024-2036), millions USD. 161
  • Table 72. Neuromorphic Computing and Sensing Market Size and Forecast, in Asia-Pacific (2024-2036), millions USD. 162
  • Table 73. Neuromorphic Computing and Sensing Market Size and Forecast, in the Rest of the World (2024-2036), millions USD.  164
  • Table 74. Neuromorphic Chip Manufacturers and Their Product Offerings.         166
  • Table 75. Neuromorphic Sensor Manufacturers and Their Product Offerings.    166
  • Table 76. Emerging Non-Volatile Memory (eNVM) Manufacturers and Their Product Offerings.              167
  • Table 77. Neuromorphic Software and Framework Providers and Their Solutions.         168
  • Table 78. Key Research Institutions and Academia in Neuromorphic Computing and Sensing.             168
  • Table 79. Competing Emerging Technologies for Neuromorphic Computing and Sensing.        171
  • Table 80. Technology Substitution Analysis.             175
  • Table 81. Comparative Advantages/Disadvantages.           177
  • Table 82. Evolution of Apple Neural Engine.              191
  • Table 83. Dynex subscription plans.               225

 

List of Figures

  • Figure 1. Global market for neuromorphic computing and sensors, 2023-2036 (Millions USD).            21
  • Figure 2. Neuromorphic computing and sensing technology roadmap. 39
  • Figure 3. Market value chain for neuromorphic computing and sensing.               43
  • Figure 4. Neuromorphic computing and sensing market map.     46
  • Figure 5. Evolution of the main hardware technologies for neuromorphic computing. 61
  • Figure 6. Key materials in NVM technology for neuromorphic computing.            63
  • Figure 7. Advanced memristive materials for neuromorphic computing.              64
  • Figure 8. Neural networks in autonomous vehicles.            72
  • Figure 9.  Concept illustration of centralized and decentralized intelligence in robotics.            75
  • Figure 10.  Neuromorphic programmable robot with dynamic vision developed by SynSense.               76
  • Figure 11. Comparison of High-Level Conventional and Neuromorphic Memory Architectures.            81
  • Figure 12. Spiking Neural Network (SNN) Structure and Operation.          81
  • Figure 13. IBM TrueNorth Processor.              88
  • Figure 14. Event-Based Sensor Operation and Data Processing Flow.     96
  • Figure 15. Conventional sensor vs. Event-based sensor.  97
  • Figure 16. Operation of neuromorphic vision sensors.       98
  • Figure 17. Cyranose 320 Electronic Nose.  99
  • Figure 18. Alpix-Pilatus platform, an integrated event-based vision sensor that combines static and dynamic information.              101
  • Figure 19. Commercial Application Milestones 2024-2025            112
  • Figure 20. Application Market Penetration Timeline             112
  • Figure 21. Technology roadmap for neuromorphic computing and sensing in mobile and consumer applications.  113
  • Figure 22. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Mobile and Consumer Applications (2024-2036), millions USD.           118
  • Figure 23. Technology Roadmap for Neuromorphic Computing and Sensing in Automotive and Transportation.             119
  • Figure 24. Sensors used by the ADAS (Advanced Driver-Assistance System).    120
  • Figure 25. Enabling technologies for autonomous vehicles.           122
  • Figure 26. Autonomous Vehicle Architecture with Neuromorphic Computing and Sensing.    123
  • Figure 27. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Automotive and Transportation (2024-2036), millions USD. 127
  • Figure 28. Technology roadmap for neuromorphic computing and sensing in industrial and manufacturing.            128
  • Figure 29. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Industrial and Manufacturing (2024-2036), millions USD. 134
  • Figure 30. Technology roadmap for neuromorphic computing and sensing in healthcare and medical devices.             135
  • Figure 31. Wearable Medical Devices with Neuromorphic Computing and Sensing Capabilities.        137
  • Figure 32. Flexible neuromorphic electronics for neuromorphic computing, humanoid robotics, and neuroprosthetics.       140
  • Figure 33. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Healthcare and Medical Devices (2024-2036), millions USD.           142
  • Figure 34. Technology roadmap for neuromorphic computing and sensing in aerospace and defense.                143
  • Figure 35. Schematic route from bio-inspired behaviours toward neuromorphic sensors for autonomous flight.    144
  • Figure 36. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Aerospace and Defence (2024-2036), millions USD.              150
  • Figure 37. Technology roadmap for neuromorphic computing and sensing in Datacenters and Cloud Services.           151
  • Figure 38. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Datacenters and Cloud Services (2024-2036), millions USD.              158
  • Figure 39. Global Neuromorphic Computing and Sensing Market Size and Forecast, in North America (2024-2036), millions USD.  160
  • Figure 40. Global Neuromorphic Computing and Sensing Market Size and Forecast, in Europe (2024-2036), millions USD. 161
  • Figure 41. Global Neuromorphic Computing and Sensing Market Size and Forecast, Asia-Pacific (2024-2036), millions USD. 163
  • Figure 42. Global Neuromorphic Computing and Sensing Market Size and Forecast, in in the Rest of the World (2024-2036), millions USD.   164
  • Figure 43. Neuromorphic Computing and Sensing Ecosystem Overview.             165
  • Figure 44. Cerebas WSE-2.   212
  • Figure 45. DeepX NPU DX-GEN1.     223
  • Figure 46. Google TPU.            233
  • Figure 47. GrAI VIP.     235
  • Figure 48. Groq Tensor Streaming Processor (TSP).              239
  • Figure 49. DVL-5000 neuromorphic laser profiler. 248
  • Figure 50. Spiking Neural Processor               250
  • Figure 51. TROOPER robot.   252
  • Figure 52. 11th Gen Intel® Core™ S-Series. 254
  • Figure 53.  Intel Loihi 2 chip. 254
  • Figure 54. Envise.        263
  • Figure 55. Pentonic 2000.      267
  • Figure 56. Azure Maia 100 and Cobalt 100 chips.  273
  • Figure 57. Mythic MP10304 Quad-AMP PCIe Card.              277
  • Figure 58. Nvidia H200 AI chip.           293
  • Figure 59. Grace Hopper Superchip.               294
  • Figure 60. Prophesee Metavision starter kit – AMD Kria KV260 and active marker LED board. 307
  • Figure 61. Cloud AI 100.         309
  • Figure 62. Overview of SpiNNaker2 architecture for the ”SpiNNcloud” cloud system and edge systems.                333
  • Figure 63. Untether AI chip.  349

 

 

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The Global Neuromorphic Computing & Sensing Market 2026-2036
The Global Neuromorphic Computing & Sensing Market 2026-2036
PDF download/by email.
Price: £1,100.00

The Global Neuromorphic Computing & Sensing Market 2026-2036
The Global Neuromorphic Computing & Sensing Market 2026-2036
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